O-176 USING THE ECHO MODEL TO TEACH OCCUPATIONAL AND ENVIRONMENTAL MEDICINE TO PRIMARY CARE PROVIDERS
Bibliographic record
Abstract
Abstract Introduction Primary care providers (PCPs) have scarce access to specialists in occupational and environmental medicine (OEM) regarding return to work, occupational injuries, illnesses, and environmental exposures. We conducted a pilot project to develop, implement and evaluate a teaching program on OEM for PCPs. The program also sought to improve communication between PCPs and the Ontario Workplace Safety and Insurance Board (WSIB) who provide wage-loss benefits and support for return to work after a work-related injury or illness. Methods We used the ECHO model to connect PCPs with OEM experts, using weekly video conference sessions to discuss participants’ cases and deliver didactic presentations. We invited PCPs to attend two cycles of 12 ECHO OEM sessions. An observational pre-post study design assessed changes in ECHO participants. Data were collected using online questionnaires for demographics, satisfaction, self-efficacy, knowledge, attitudes and beliefs. We analyzed the data using parametric and non-parametric statistics. Two cycles of 12 sessions each ran from September 2021 - June 2022, attended by various PCPs. Results 229 participants registered, and 150 attended at least one session, of these 67 completed both pre- and post-ECHO questionnaires. ECHO OEM had a positive impact on satisfaction; self-efficacy increased post-ECHO (p<.0001) and knowledge increased after ECHO (p=0.004). Two of 10 items regarding attitudes and beliefs, related to perceptions, and the role of the WSIB significantly improved after ECHO (p<0.05). ECHO OEM was feasible and acceptable in Ontario. Discussion and Conclusion We demonstrated high satisfaction with the program, improved self-efficacy, increased knowledge, and improved attitudes and beliefs about the WSIB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".