Development of a mentorship strategy: A knowledge translation case study
Bibliographic record
Abstract
INTRODUCTION: There are many theories and frameworks for achieving knowledge translation, and the assortment can be confusing to those responsible for planning, evaluation, or policymaking in knowledge translation. A conceptual framework developed by Graham and colleagues provides an approach that builds on the commonalities found in an assessment of planned-action theories. This article describes the application of this knowledge to action framework to a mentorship initiative in academic medicine. Mentorship influences career success but is threatened in academia by increased clinical, research, and administrative demands. METHODS: A case study review was undertaken of the role of mentors, the experiences of mentors and mentees, and mentorship initiatives in developing and retaining clinician scientists at two universities in Alberta, Canada. This project involved relevant stakeholders including researchers, university administrators, and research funders. RESULTS: The knowledge to action framework was used to develop a strategy for mentorship for clinician researchers. The framework highlights the need to identify and engage stakeholders in the process of knowledge implementation. A series of initiatives were selected and tailored to barriers and facilitators to implementation of the mentorship initiative; strategies for evaluating the knowledge use and its impact on outcomes were developed. DISCUSSION: The knowledge to action framework can be used to develop a mentorship initiative for clinician researchers. Future work to evaluate the impact of this intervention on recruitment and retention is planned.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".