WPLN News12%

After concerning audit, Nashville mulls more oversight of opioid settlement funds 15%

By Catherine Sweeney13%

7/23/2026, 1:47:47 PM

BS Summary: This article contains 20 faulty reasoning types, including Ambiguity (Equivocation), Negativity Bias, and Appeal to Authority, with Anecdotal as the most egregious example at 8.4% saturation with 83 hits. Analysis detected 650 faulty-reasoning hits from 988 analyzed words, generating a BS Score of 30.9% and a BS Rank of 15% (18,768 of 21,887 articles). This article is better (less manipulative) than 85.70% of the article peer group.

Nashville has received more than $13 million dollars from opioid makers, sellers and marketers . 
This summer, the city released an unflattering internal audit of the how the settlement funds are being used. 
That’s why a handful of Metro Council members want to put more oversight in place. 
Among other things, their proposal would create a public dashboard that shows how the money is being spent and would require regular reports on how the programming is going. 
Nashville is one of thousands of cities and counties that have gotten opioid settlement funding from lawsuits, which alleged the drug companies knowingly created a deadly addiction epidemic. 
The money is designed to alleviate the fallout from the opioid epidemic  a cause that’s open to interpretation. 
In Nashville, the Metro Public Health Department is overseeing the money. 
Its goal is to connect people with substance use treatment before they overdose. 
That’s largely through a continuum of care program. 
The department brought in the Mental Health Cooperative and Samaritan Recovery Community to provide services. 
The internal audit, released in June, says there is no question whether that’s an appropriate use of the money. 
That has been an issue in other communities across the country and here in Tennessee . 
But it did raise several concerns about whether the money is being managed well. 
The audit organizes the problems it identified by severity, and it says the issue posing the highest risk was the use of funding for staff salaries. 
In fiscal 2025, the department spent almost $3 million on opioid programming, and half of that went to staff salaries. 
The rest went to things like treatment providers, medical supplies and IT. 
The audit says the department failed to adequately track how its employees were spending their time, which meant that the money could have been paying for work unrelated to opioid abatement. 
Auditors accused health department managers of interfering with investigations into that problem. 
Auditors contacted 34 department employees, asking for written answers to questions about their titles, primary responsibilities, how they’re connected to opioid initiatives and how much of their time they spend on opioid-related work. 
Under city codes, the report says, auditors are allowed to hear directly from workers without management seeing or weighing in on the communications. 
“Emails obtained showed that the program manager reviewed, edited, and provided directions to employees regarding their responses prior to submission,” the report reads. 
“In some cases, employees were instructed to revise their descriptions of duties or estimates of time spent on opioid-related work before sending their responses. 
Employees were also instructed to share written responses with the program manager prior to sending them to the auditors.” 
The other concern is about oversight more generally. 
There were mistakes on payments to contractors. 
For example, an invoice of more than $100,000 was paid to Mental Health Cooperative, even though it was meant for the Samaritan Recovery Community. 
The auditors say the department isn’t doing a good job measuring how programs are working over time. 
Essentially, the numbers the department uses  the number of overdoses in the community and whether they’re decreasing, or the number of people served  aren’t specific enough, the report indicates. 
“When performance measures are not consistently tracked, evaluated, and used to inform decision-making, the risk that program effectiveness cannot be fully assessed increases,” the report reads. 
“Additionally, opportunities to improve outcomes and maximize the impact of opioid settlement funds may be missed.” 
Metro Council resolution 
That’s why the Metro Council is considering legislation to make some major changes. 
One of the bill’s authors, Councilmember Erin Evans, told the Public Health and Safety Committee this week that the policy is a work in progress, and that she had a meeting scheduled with the health department to talk about the particulars. 
“I don’t feel like this is adversarial to the health department,” she said during the hearing. 
“It’s really just how can we make this a stronger process?” 
As it’s written now, the measure would shift some of the money management  accounting, financial monitoring and compliance reporting  to Metro’s finance department. 
It would have the finance department either hire someone or designate a current employee to be the opioid settlement coordinator. 
That person would, among other things, monitor more specific performance metrics and build a communication process with Metro and other government officials. 
The finance and health departments would be given a few months to build an Opioid Settlement Transparency Dashboard. 
That would display all ongoing projects and associated standardized metrics, provide visualizations and downloadable data, and document data sources and show program schedules. 
The measure was on the agenda for Tuesday’s council meeting, but Evans deferred for a week to get some more feedback. 
Other communities and opioid settlement funds 
Across the country , these settlement funds have fueled controversy . 
When local governments get federal money for similar programs, from agencies like the Centers for Disease Control and Prevention, there are strings attached. 
There are strict rules on how the money can be spent, and federal bureaucrats keep an eye on whether those rules are being followed. 
That structure doesn’t exist for opioid settlement money. 
There is no federal oversight. 
The money has to be used to address the fallout from the opioid epidemic, but that’s pretty vague and subjective. 
The opioid companies aren’t paying attention. 
So local governments and even states are on their own. 
KFF Health News has been covering this issue for years . 
That included a story about Greene County , in northeastern Tennessee. 
That jurisdiction used opioid funding to pay down county debt, build infrastructure, and buy a pickup truck to haul jail inmates to street cleaning sites. 
When local governments make decisions like that, they often argue their city or county has been shouldering the costs of the opioid epidemic for years, and they deserve to be paid back. 
Article reasoning-pattern comparisonThis article: 0.0%Catherine Sweeney: 1.4%WPLN: 2.3%Confirmation Bias0.0%This article: 0.0%Catherine Sweeney: 0.2%WPLN: 0.7%Anchoring Bias0.0%This article: 4.5%Catherine Sweeney: 2.8%WPLN: 3.8%Availability Heuristic4.5%This article: 0.0%Catherine Sweeney: 0.2%WPLN: 1.1%Representativeness Heuristic0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.3%Hindsight Bias0.0%This article: 2.4%Catherine Sweeney: 1.8%WPLN: 0.8%Overconfidence Bias2.4%This article: 0.0%Catherine Sweeney: 1.7%WPLN: 4.0%Framing Effect0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.4%Loss Aversion0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.7%Status Quo Bias0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Sunk Cost Effect0.0%This article: 4.1%Catherine Sweeney: 0.7%WPLN: 2.8%Optimism Bias4.1%This article: 3.1%Catherine Sweeney: 0.5%WPLN: 1.0%Pessimism Bias3.1%This article: 7.4%Catherine Sweeney: 12.5%WPLN: 7.4%Negativity Bias7.4%This article: 1.6%Catherine Sweeney: 2.7%WPLN: 1.0%Self-Serving Bias1.6%This article: 0.6%Catherine Sweeney: 0.3%WPLN: 0.3%Fundamental Attribution Error0.6%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Actor-Observer Bias0.0%This article: 3.2%Catherine Sweeney: 0.9%WPLN: 0.9%In-Group Bias3.2%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Out-Group Homogeneity Bias0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 2.4%Halo Effect0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.2%Horn Effect0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Catherine Sweeney: 0.2%WPLN: 1.6%Recency Bias0.0%This article: 1.1%Catherine Sweeney: 0.2%WPLN: 0.2%Primacy Effect1.1%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Blind-Spot Bias0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.3%Ad Hominem0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Straw Man0.0%This article: 5.5%Catherine Sweeney: 3.2%WPLN: 3.2%Appeal to Authority5.5%This article: 0.8%Catherine Sweeney: 0.1%WPLN: 1.0%False Dilemma0.8%This article: 0.0%Catherine Sweeney: 0.5%WPLN: 0.8%Slippery Slope0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Circular Reasoning0.0%This article: 2.7%Catherine Sweeney: 2.9%WPLN: 3.9%Hasty Generalization2.7%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.2%Red Herring0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.3%Bandwagon0.0%This article: 0.0%Catherine Sweeney: 5.0%WPLN: 4.7%Appeal to Emotion0.0%This article: 1.9%Catherine Sweeney: 1.2%WPLN: 0.6%Begging the Question1.9%This article: 3.1%Catherine Sweeney: 1.1%WPLN: 3.1%Post Hoc (False Cause)3.1%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.0%Tu Quoque0.0%This article: 0.0%Catherine Sweeney: 0.8%WPLN: 0.8%Burden of Proof0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Appeal to Nature0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.2%Composition/Division0.0%This article: 8.4%Catherine Sweeney: 2.4%WPLN: 2.9%Anecdotal8.4%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.0%No True Scotsman0.0%This article: 7.6%Catherine Sweeney: 1.3%WPLN: 1.9%Ambiguity (Equivocation)7.6%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.0%Gambler’s Fallacy0.0%This article: 1.1%Catherine Sweeney: 0.5%WPLN: 0.1%Middle Ground1.1%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.0%Personal Incredulity0.0%This article: 0.0%Catherine Sweeney: 0.3%WPLN: 0.1%Special Pleading0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Genetic Fallacy0.0%This article: 2.3%Catherine Sweeney: 1.5%WPLN: 1.5%Unattributed Quote2.3%This article: 2.3%Catherine Sweeney: 1.0%WPLN: 0.7%Quote-first Misdirection2.3%This article: 1.8%Catherine Sweeney: 1.2%WPLN: 1.9%Biased Writer Voice1.8%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.9%Indoctrination0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.6%Politically Left Leaning Bias0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Catherine Sweeney: 0.0%WPLN: 1.6%Attempt to Sell a Product or S…0.0%

988 words analyzed.

Speakers

1speaker6.9%attributed speech920writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageErin Evans • 41 words • 0.0% coverageErin Evans • 16 words • 0.0% coverageErin Evans • 11 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 32 words • 0.0% coverage
Selected voice

Erin Evans

100%flagged-word coverage
68 attributed words100% of attributed speech42% writer coverage
0%2.5%5.0%Unattributed Quote-2.5 ptsWriter: 2.5%Erin Evans: 0.0%0.0%Quote-first Misdirection-2.5 ptsWriter: 2.5%Erin Evans: 0.0%0.0%Biased Writer Voice-2.0 ptsWriter: 2.0%Erin Evans: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.