Exploring mentorship as a strategy to build capacity for knowledge translation research and practice: a scoping systematic review
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) supports use of evidence in healthcare decision making but is not widely practiced. Mentoring is a promising means of developing KT capacity. The purpose of this scoping systematic review was to identify essential components of mentoring that could be adapted for KT mentorship. METHODS: Key social sciences and management databases were searched from January 2002 to December 2011 inclusive. Empirical research in non-healthcare settings that examined mentorship design and impact for improving job-specific knowledge and skill were eligible. Members of the study team independently selected eligible studies, and extracted and summarized data. RESULTS: Of 2,101 search results, 293 were retrieved and 13 studies were eligible for review. All but one reported improvements in knowledge, skill, or behavior. Mentoring program components included combining preliminary workshop-based training with individual mentoring provided either in person or remotely; training of mentors; and periodic mentoring for at least an hour over a minimum period of six months. Barriers included the need for infrastructure for recruitment, matching, and training; lack of clarity in mentoring goals; and limited satisfaction with mentors and their availability. Findings were analyzed against a conceptual framework of factors that influence mentoring design and impact to identify issues warranting further research. CONCLUSION: This study identified key mentoring components that could be adapted for KT mentorship. Overall, few studies were identified. Thus further research should explore whether and how mentoring should be tailored to baseline knowledge or skill and individual KT needs; evaluate newly developed or existing KT mentorship programs based on the factors identified here; and examine whether and how KT mentorship develops KT capacity. The conceptual framework could be used to develop or evaluate KT mentoring programs.
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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 | 0.138 | 0.295 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.032 | 0.029 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".