Health care providers’ experiences and perceptions participating in a chronic pain telementoring education program: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Chronic pain affects one in five Canadians. Frontline health care providers (HCPs) manage the majority of patients with chronic pain yet receive minimal training to do so. The Extension for Community Healthcare Outcomes (ECHO) model™ is an education intervention aimed at HCPs (not patients) to support and improve care in underserviced communities. ECHO Ontario Chronic Pain and Opioid Stewardship (ECHO PAIN) is an adaptation of the ECHO model where the program goals are to support and improve chronic pain and opioid management in the province of Ontario, Canada. AIMS: This study aimed to investigate the perceptions of HCPs participating in ECHO PAIN. METHODS: Thirteen HCPs attending ECHO PAIN participated in in-depth semistructured phone interviews. Resulting data were analyzed through a qualitative descriptive lens. RESULTS: Analysis uncovered four themes: (1) HCPs' motivation for joining ECHO PAIN, (2) interprofessional collaboration through ECHO PAIN, (3) the use of opioids for pain management, and (4) barriers and facilitators to participation and satisfaction in ECHO PAIN. HCPs joined ECHO PAIN because of their struggles managing their complex patients with chronic pain. HCPs also recognized the importance of interprofessional collaboration in pain management and shared examples of integration of different professional approaches in their clinical teams. Opioids for pain management remained a controversial issue, and ECHO served as an opportunity to decrease this knowledge gap. Finally, HCPs described how time constraints, organizational support, and session structure acted as barriers to their participation and satisfaction in the ECHO PAIN program; technology mediated satisfaction. CONCLUSIONS: This study was the first in Canada to explore the motivations of HCPs in attending a chronic pain telementoring program as well as to examine the interprofessional effects of participation. HCPs increased their knowledge about management of chronic pain and increased their interprofessional approach.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".