Canadian environmental health officer perceptions of barriers to research utilization in everyday and emergency practice
Bibliographic record
Abstract
Understanding the barriers to research utilization (RU) experienced by Environmental Health Officers (EHOs) facilitates evidence use and improved outcomes in both normal and emergency practice. The purposes of this study were to (i) understand the barriers to research use in the everyday work of EHOs and (ii) determine how these barriers change in the context of emergency practice. The Barriers to Research Utilization (BARRIERS) Scale was disseminated to a cross-sectional sample of Canadian EHOs. Responses were analyzed using measures of central tendency. Data were collected during a typical work period in 2012 (311 respondents) and during the COVID-19 pandemic in 2020 (82 respondents). The three greatest barriers to RU identified by EHOs at both points in time were (i) a lack of authority to implement changes in practice, (ii) a lack of time to review research, and (iii) a lack of time to implement research findings. Mean ratings were not statistically different in 2012 and 2020. The consistency of the top three barriers to RU suggests they are particularly embedded in EHO practice in Canada, indicating that they are useful intervention targets to increase RU. Further, it can be inferred that targeted interventions to support RU will benefit outcomes in both normal and emergency situations. More research is needed to understand the embedded structural and organizational issues influencing EHO practice behaviour.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".