Project ECHO Occupational and Environmental Medicine: A Qualitative Study of HealthCare Providers Supporting Workers with Work-Related Injuries and Illnesses
Bibliographic record
Abstract
PURPOSE: This qualitative study investigated the needs, barriers, and facilitators that affect primary care providers' involvement in supporting patients' stay-at-work and return-to-work following injury or illness. It also aims to understand the lived experiences of primary care providers who participated in the Extension for Community Healthcare Outcomes training program for Occupational and Environmental Medicine (ECHO OEM). By examining both the structural and experiential aspects of the program, this study seeks to provide insights into how ECHO OEM influences providers' approaches to occupational health challenges. METHODS: Those who attended ECHO OEM sessions were invited to participate in the research study. Four focus groups and five one-on-one interviews were conducted with healthcare providers participating in ECHO OEM. Audio-recordings were transcribed verbatim and analyzed using an inductive thematic analysis approach. This study was structured according to the COREQ Checklist. RESULTS: We discussed six main themes: (1) Challenges with Engaging with Workers' Compensation Boards; (2) Return to Work practices; (3) Health and Well-Being; (4) Communication is Important; (5) Perspective from the Workplace; and (6) Feedback on ECHO OEM. CONCLUSION: ECHO OEM sessions contribute to and impact healthcare providers' knowledge of supporting injured or ill workers. Topics that deserve further attention include incorporating comorbid physical and mental health conditions, navigating workers' compensation systems, and supporting specific populations such as military veterans and emergency personnel.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".