Recognition of knowledge translation practice in Canadian health sciences tenure and promotion: A content analysis of institutional policy documents
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: There has been growing emphasis on increasing impacts of academic health research by integrating research findings in healthcare. The concept of knowledge translation (KT) has been widely adopted in Canada to guide this work, although lack of recognition in tenure and promotion (T&P) structures have been identified as barrier to researchers undertaking KT. Our objective was to explore how KT is considered in institutional T&P documentation in Canadian academic health sciences. METHODS: We conducted content analysis of T&P documents acquired from 19 purposively sampled research-intensive or largest regional Canadian institutions in 2020-2021. We coded text for four components of KT (synthesis, dissemination, exchange, application). We identified clusters of related groups of documents interpreted together within the same institution. We summarized manifest KT content with descriptive statistics and identified latent categories related to how KT is considered in T&P documentation. RESULTS: We acquired 89 unique documents from 17 institutions that formed 48 document clusters. Most of the 1057 text segments were categorized as dissemination (n = 851, 81%), which was included in 47 document clusters (98%). 15 document clusters (31%) included all four KT categories, while one (2%) did not have any KT categories identified. We identified two latent categories: primarily implicit recognition of KT; and an overall lack of clarity on KT. CONCLUSIONS: Our analysis of T&P documents from primarily research-intensive Canadian universities showed a lack of formal recognition for a comprehensive approach to KT and emphasis on traditional dissemination. We recommend that institutions explicitly and comprehensively consider KT in T&P and align documentation and procedures to reflect these values.
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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 | 0.032 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.031 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".