The role of mentoring and coaching of healthcare professionals for digital technology adoption and implementation: A scoping review
Bibliographic record
Abstract
Objective: Mentoring and coaching practices have supported the career and skill development of healthcare professionals (HCPs); however, their role in digital technology adoption and implementation for HCPs is unknown. The objective of this scoping review was to summarize information on healthcare education programs that have integrated mentoring or coaching as a key component. Methods: The search strategy and keyword searches were developed by the project team and a research librarian. A two-stage screening process consisting of a title/abstract scan and a full-text review was conducted by two independent reviewers to determine study eligibility. Articles were included if they: (1) discussed the mentoring and/or coaching of HCPs on digital technology, including artificial intelligence, (2) described a population of HCPs at any stage of their career, and (3) were published in English. Results: A total of 9473 unique citations were screened, identifying 19 eligible articles. 11 articles described mentoring and/or coaching programs for digital technology adoption, while eigth described mentoring and/or coaching for digital technology implementation. Program participants represented a diverse range of industries (i.e., clinical, academic, education, business, and information technology). Digital technologies taught within programs included electronic health records (EHRs), ultrasound imaging, digital health informatics, and computer skills. Conclusions: This review provided a summary of the role of mentoring and/or coaching practices within digital technology education for HCPs. Future training initiatives for HCPs should consider appropriate resources, program design, mentor-learner relationship, security concerns and setting clear expectations for program participants. Future research could explore mentor/coach characteristics that would facilitate successful skill transfer.
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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 | 0.040 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".