Exploring mentorship as a strategy to build capacity for knowledge translation research and practice: protocol for a qualitative study
Bibliographic record
Abstract
BACKGROUND: Research funders, educators, investigators and decision makers worldwide have identified the need to improve the quality of health care by building capacity for knowledge translation (KT) research and practice. Peer-based mentorship represents a vehicle to foster KT capacity. The purpose of this exploratory study is to identify mentoring models that could be used to build KT capacity, consult with putative mentee stakeholders to understand their KT mentorship needs and preferences, and generate recommendations for the content and format of KT mentorship strategies or programs, and how they could be tested through future research. METHODS: A conceptual framework was derived based on mentoring goals, processes and outcomes identified in the management and social sciences literature, and our research on barriers and facilitators of academic mentorship. These concepts will inform data collection and analysis. To identify useful models by which to design, implement and evaluate KT mentorship, we will review the social sciences, management, and nursing literature from 1990 to current, browse tables of contents of relevant journals, and scan the references of all eligible studies. Eligibility screening and data extraction will be performed independently by two investigators. Semi-structured interviews will be used to collect information about KT needs, views on mentorship as a knowledge sharing strategy, preferred KT mentoring program elements, and perceived barriers from clinician health services researchers representing different disciplines. Qualitative analysis of transcripts will be performed independently by two investigators, who will meet to compare findings and resolve differences through discussion. Data will be shared and discussed with the research team, and their feedback incorporated into final reports. DISCUSSION: These findings could be used by universities, research institutes, funding agencies, and professional organizations in Canada and elsewhere to develop, implement, and evaluate mentorship for KT research and practice. This research will establish a theoretical basis upon which we and others can compare the cost-effectiveness of interventions that enhance KT mentorship. If successful, this program of research may increase knowledge about, confidence in, and greater utilization of KT processes, and the quality and quantity of KT research, perhaps ultimately leading to better implementation and adoption of recommended health care services.
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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 | 0.133 | 0.119 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.042 | 0.011 |
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".