Interprofessional Discussion for Knowledge Transfer in a Digital “Community of Practice” for Managing Pneumoconiosis: Mixed Methods Study
Bibliographic record
Abstract
Background: Pneumoconiosis prevalence is increasing in the United States, especially among coal miners. Contemporaneously with an increased need for specialized multidisciplinary care for miners, there is a shortage of experts to fulfill this need. Miners' Wellness ECHO (Extension for Community Health Outcomes) is a digital community of practice based on interprofessional discussion for knowledge transfer. The program has been demonstrated to increase participants' self-efficacy for clinical, medicolegal, and "soft" skills related to miners' health. Objective: We aimed to examine characteristics associated with interprofessional discussions and suggest ways to strengthen knowledge transfer. Methods: This mixed methods study used an exploratory sequential design. We video-recorded and transcribed ECHO sessions over 14 months from July 2018 to September 2019 and analyzed content to examine participant discussions. We focused on participants' statements of expertise followed by other participants' acceptance or eschewal of these statements (utterances). We conducted quantitative analyses to examine the associations of active participation in discussion (primary outcome variable, defined as any utterance). We analyzed the association of the outcome on the following predictors: (1) participant group status, (2) study time frame, (3) participant ECHO experience status, (4) concordance of participant group identity between presenter and participant, (5) video usage, and (6) attendance frequency. We used the generalized estimating equations approach for longitudinal data, logit link function for binary outcomes, and LSMEANS to examine least squares means of fixed effects. Results: We studied 23 sessions with 158 unique participants and 539 total participants, averaging 23.4 (SD 5.6) participants per session. Clinical providers, the largest participant group, constituting 36.7% (n=58) of unique participants, were the most vocal group (mean 21.74, SD 2.11 average utterances per person-session). Benefits counselors were the least vocal group, with an average utterance rate of 0.57 (SD 0.29) per person-session and constituting 8.2% (n=13) of unique participants. Thus, various participant groups exhibited different utterance rates across sessions (P=.003). Experienced participants may have dominated active participation in discussion compared to those with less or intermediate experience, but this difference was not statistically significant (P=.11). When the didactic presenter and participant were from the same participant group, active participation by the silent group participants was greater than when both were from different groups. This association was not seen in vocal group participants (interaction P=.003). Compared to those participating by audio, those participating on video tended to have higher rates of active participation, but this difference was not statistically significant (P=.11). Conclusions: Our findings provide insight into the mechanics of interprofessional discussion in a digital community of practice managing pneumoconiosis. Our results underscore the capacity of the novel ECHO model to leverage technology and workforce diversity to facilitate interprofessional discussions on the multidisciplinary care of miners. Future research will evaluate whether this translates into improved patient outcomes.
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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 | 0.039 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".