The dimensional structure of the barriers to research utilization experienced by Environmental Health Officers in Canada
Bibliographic record
Abstract
Background The purpose of this study was to explore the dimensional structure of the Environmental Health Officer (EHO) responses to the BARRIERS Scale from data collected in 2012 and 2020 to: (1) uncover new holistic ways of interpreting the factors influencing research utilization (RU); and (2) apply a theoretical lens to the data. Methods Principal Component Analysis was used to analyze the dimensional structure of the 2012 data and develop a structural equation model (SEM). The resultant factors were categorized using the Active Implementation Frameworks (AIFs). Confirmatory Factor Analysis was applied to the SEM using the 2020 data. Results A four factor solution was identified and the resultant SEM aligned with the following AIFs: Competency Drivers, Useful Innovations, Leadership Drivers, and Organization Drivers. The SEM was found to explain 52% of the loading variation, capturing a satisfactory amount of information from the dataset. The SEM was analyzed for goodness of fit using 2020 data and it showed a statistically sound but imperfect fit. Conclusions These four factors provide a useful way to understand and mitigate barriers to RU in EHO practice. Three of the factors align with the Implementation Drivers Framework (Competency Drivers, Leadership Drivers, and Organization Drivers) and the fourth factor was associated with the Useful Innovations Framework, representing only two of the six AIFs, thus demonstrating a theoretical gap. The imperfect goodness of fit test indicated a statistical gap. These results suggest that more research is needed to better understand the full set of barriers to RU experienced by EHOs.
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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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".