Health researchers in Alberta: an exploratory comparison of defining characteristics and knowledge translation activities
Bibliographic record
Abstract
BACKGROUND: Canadian funding agencies are no longer content to support research that solely advances scientific knowledge, and key directives are now in place to promote research transfer to policy- and decision-makers. Therefore, it is necessary to improve our understanding of how researchers are trained and supported to facilitate knowledge translation activities. In this study, we investigated differences in health researcher characteristics and knowledge translation activities. METHODS: Our sample consisted of 240 health researchers from three Alberta universities. Respondents were classified by research domain [basic (n = 72) or applied (n = 168)] and faculty [medical school (n = 128) or other health science (n = 112)]. We examined our findings using Mode I and Mode II archetypes of knowledge production, which allowed us to consider the scholarly and social contexts of knowledge production and translation. RESULTS: Differences among health researcher professional characteristics were not statistically significant. There was a significant gender difference in the applied researcher faculty group, which was predominantly female (p < .05). Research domain was linked to translation activities. Applied researchers reported engaging in significantly more Mode II activities than basic researchers (p < .001), and scored higher than basic researchers regarding the perceived importance of translation activities (Mode I, p = .01; Mode II, p < .001). Main effects of faculty were limited to engaged dissemination (medical school < other faculties; p = .025) and number of publications (medical school > other faculties; p = .004). There was an interaction effect for research domain and faculty group for number of publications (p = .01), in that applied researchers in medical faculties published more than their peers in other faculty groups. CONCLUSION: Our findings illustrate important differences between health researchers and provide beginning insights into their professional characteristics and engagement in Mode I and Mode II activities. A future study designed to examine these dimensions in greater detail, including potential covariates across more varied institutions, would yield richer insights and enable an examination of relative influences, needs and costs of each mode of activity.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".