Transfer of Knowledge on Pneumoconiosis Care Among Rural-Based Members of a Digital Community of Practice: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Given the re-emergence of coal workers' pneumoconiosis in Appalachia and Mountain West United States, there is a tremendous need to train rural professionals in its multidisciplinary management. Since 2016, the Miners' Wellness TeleECHO (Extension for Community Health Outcomes) Program held by the University of New Mexico, Albuquerque, and Miners' Colfax Medical Center, Raton, New Mexico, provides structured longitudinal multidisciplinary telementoring to diverse professionals taking care of miners by creating a digital community of practice. Program sessions emphasize active learning through discussion, rather than didactic training. Professional stakeholder groups include respiratory therapists, home health professionals, benefits counselors, lawyers or attorneys, clinicians, and others. Rural-urban differences in knowledge transfer in such a community of practice, however, remain unknown. OBJECTIVE: We aim to evaluate the role of the rurality of the patient or client base in the transfer of knowledge to professionals caring for miners using the digital community of practice approach. METHODS: This is a cross-sectional study of 70 professionals participating in the Miners' Wellness TeleECHO Program between 2018 and 2019. Drawing insights from social network analysis, we examined the association between the rurality of participants' patient or client base and their self-reported receipt of knowledge. Our focal independent variable was the respondent's self-reported percentage of patients or clients who reside in rural areas. We measured knowledge transfer sources by asking participants if they received knowledge regarding the care of miners during and outside of TeleECHO sessions from each of the other participants. Our dependent variables included the number of knowledge sources, number of cross-stakeholder knowledge sources, number of same stakeholder knowledge sources, and range and heterogeneity of knowledge sources. RESULTS: Respondents, on average, identified 4.46 (SD 3.16) unique knowledge sources within the community, with a greater number of cross-stakeholder knowledge sources (2.80) than same stakeholder knowledge sources (1.72). The mean knowledge source range was 2.50 (SD 1.29), indicating that, on average, respondents received knowledge sources from roughly half of the 5 stakeholder groups. Finally, the mean heterogeneity of knowledge sources, which can range between 0 and 0.80, was near the midpoint of the scale at 0.44 (SD 0.30). Multivariable analyses revealed that as the rurality of patient or client bases increased, participants reported more knowledge sources overall, more knowledge sources from outside of their stakeholder groups, a higher knowledge source range, and greater heterogeneity of knowledge sources (P<.05 for all comparisons). CONCLUSIONS: Our findings suggest that participants who serve rural areas especially benefit from knowledge transfer within the TeleECHO community of practice. Additionally, the knowledge they receive comes from diverse information sources, emphasizing its multidisciplinary nature. Our results underscore the capacity of the TeleECHO model to leverage technology to promote rural health equity for miners.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".