Evaluation of a Pilot Program to Prevent the Misuse of Prescribed Opioids Among Health Care Workers: Repeated Measures Survey Study
Bibliographic record
Abstract
BACKGROUND: Overprescription of opioids has led to increased misuse of opioids, resulting in higher rates of overdose. The workplace can play a vital role in an individual's intentions to misuse prescription opioids with injured workers being prescribed opioids, at a rate 3 times the national average. For example, health care workers are at risk for injuries, opioid dispensing, and diversion. Intervening within a context that may contribute to risks for opioid misuse while targeting individual psychosocial factors may be a useful complement to interventions at policy and prescribing levels. OBJECTIVE: This pilot study assessed the effects of a mobile-friendly opioid misuse intervention prototype tailored for health care workers using the preparation phase of a multiphase optimization strategy design. METHODS: A total of 33 health care practitioners participated in the pilot intervention, which included 10 brief web-based lessons aimed at impacting psychosocial measures that underlie opioid misuse. The lesson topics included: addiction beliefs, addiction control, Centers for Disease Control and Prevention guidelines and recommendations, beliefs about patient-provider relationships and communication, control in communicating with providers, beliefs about self-monitoring pain and side effects, control in self-monitoring pain and side effects, diversion and disposal beliefs, diversion and disposal control, and a conclusion lesson. Using a treatment-only design, pretest and posttest surveys were collected. A general linear repeated measures ANOVA was used to assess mean differences from pretest to posttest. Descriptive statistics were used to assess participant feedback about the intervention. RESULTS: After completing the intervention, participants showed significant mean changes with increases in knowledge of opioids (+0.459; P<.001), less favorable attitudes toward opioids (-1.081; P=.001), more positive beliefs about communication with providers (+0.205; P=.01), more positive beliefs about pain management control (+0.969; P<.001), and increased intentions to avoid opioid use (+0.212; P=.03). Of the 33 practitioners who completed the program, most felt positive about the information presented, and almost 70% (23/33) agreed or strongly agreed that other workers in the industry should complete a program like this. CONCLUSIONS: While attempts to address the opioid crisis have been made through public health policies and prescribing initiatives, opioid misuse continues to rise. Certain industries place workers at greater risk for injury and opioid dispensing, making interventions that target workers in these industries of particular importance. Results from this pilot study show positive impacts on knowledge, attitudes, and beliefs about communicating with providers and pain management control, as well as intentions to avoid opioid misuse. However, the dropout rate and small sample size are severe limitations, and the results lack generalizability. Results will be used to inform program revisions and future optimization trials, with the intention of providing insight for future intervention development and evaluation of mobile-friendly eHealth interventions for employees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".