Collaborative practice competencies needed for telehealth delivery by health and social care professionals: a scoping review
Bibliographic record
Abstract
In the context of the COVID-19 pandemic, many healthcare and social services professionals have had to provide services through virtual care. In the workplace, such professionals often need to be sufficiently resourced to collaborate and address collaborative care barriers in telehealth. We performed a scoping review to identify the competencies required to support interprofessional collaboration among clinicians in telehealth. We followed Arksey and O'Malley's and the Joanna Briggs Institute's methodological guidelines, including quantitative and qualitative peer-reviewed articles published between 2010 and 2021. We expanded our data sources by searching for any organization or experts in the field via Google. The analysis of the resulting thirty-one studies and sixteen documents highlighted that health and social services professionals are generally unaware of the competencies they need to develop or maintain interprofessional collaboration in telehealth. In an era of digital innovations, we believe this gap may jeopardize the quality of the services offered to patients and needs to be addressed. Of the six competency domains in the National Interprofessional Competency Framework, it was observed that interprofessional conflict resolution was the competency that emerged least as an essential competency to be developed, while interprofessional communication and patient/client/family/community-centered care were identified as the two most reported essential competencies.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".