Reporting health research translation and impact in the curriculum vitae: a survey
Bibliographic record
Abstract
BACKGROUND: Increasingly, health researchers must demonstrate the impact and real-life applications of their research. We investigated how health researchers with expertise in knowledge translation report research translation activities and impact on their curriculum vitae (CV). METHODS: We conducted a cross-sectional survey of health researchers with expertise in knowledge translation as we anticipated best practices in CV reporting from this specialized group. Our survey asked participants about their reporting of research translation and impact activities on their CVs, intention to report, and barriers and facilitators to reporting such activities on their CVs. We calculated univariate descriptive statistics for all quantitative data. Linear regression models determined predictors of researchers' intention to report research translation and impact activities on their CVs. We analyzed open-ended qualitative responses using content analysis. RESULTS: One hundred and fifty-three health researchers responded to the survey (response rate = 29%). Most respondents were Canadian, were female, and had a doctoral degree. Eighty-two percent indicated they reported at least one research translation and/or impact indicator on their CVs. Of those, health researchers commonly reported the following: advisory/regulatory committee membership related to research program (83%), research translation award(s) (61%), and academic performance assessments (59%). Researchers least commonly indicated the following: citation metric scores (31%), summaries of impact (21%), and requests to use research materials and/or products (19%). Fewer than half of the health researchers intended to report knowledge translation (43%) and impact (33%) on their CVs. Strong beliefs about capabilities and consequences of reporting research translation and/or impact were significant predictors of intention. Main barriers were as follows: CV templates do not include research translation and impact activities, participants perceived employers do not value research translation and impact activities, and lack of metrics to evaluate research translation and impact. Ninety-six percent were unaware of a CV template formatted to include research translation and/or impact reporting. CONCLUSIONS: Knowledge translation and impact indicators on the CV are inconsistently reported by our sample of health researchers. Modifiable barriers should be addressed to support more consistent reporting of such activities, including providing a CV template that includes research translation and impact as well as clear metrics to quantify them.
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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 | 0.092 | 0.234 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".