Evaluation of an innovative tele-education intervention in chronic pain management for primary care clinicians practicing in underserved areas
Bibliographic record
Abstract
Introduction Inadequate knowledge and training of healthcare providers are obstacles to effective chronic pain management. ECHO (extension for community healthcare outcomes) uses case-based learning and videoconferencing to connect specialists with providers in underserved areas. ECHO aims to increase capacity in managing complex cases in areas with poor access to specialists. Methods A pre-post study was conducted to evaluate the impact of ECHO on healthcare providers’ self-efficacy, knowledge and satisfaction. Type of profession, presenting a case, and number of sessions attended were examined as potential factors that may influence the outcomes Results From June 2014 to March 2017, 296 primary care healthcare providers attended ECHO, 264 were eligible for the study, 170 (64%) completed the pre-ECHO questionnaire and 119 completed post-ECHO questionnaires. Participants were physicians (34%), nurse practitioners (21%), pharmacists (13%) and allied health professionals (32%). Participants attended a mean of 15 ± 9.19 sessions. There was a significant increase in self-efficacy ( p < 0.0001) and knowledge ( p < 0.0001). Self-efficacy improvement was significantly higher among physicians, physician assistants and nurse practitioners than the non-prescribers group ( p = 0.03). On average, 96% of participants were satisfied with ECHO. Satisfaction was higher among those who presented cases and attended more sessions. Discussion This study shows that ECHO improved providers’ self-efficacy and knowledge. We evaluated outcomes from a multidisciplinary group of providers practicing in Ontario. This diversity supports the generalisability of our findings. Therefore, we suggest that this project may be used as a template for creating other educational programs on other medical topics.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".