“We could have used a lot more of this before…”: A qualitative study understanding barriers and facilitators to implementing a provincial PPE safety coach program during the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: A Provincial PPE Safety Coach Program was introduced to support appropriate use of personal protective equipment by health care workers. The objective was to understand barriers and facilitators to implementation. METHODS: A qualitative study was conducted mid-2021. Participants were recruited using a purposive sampling strategy. Interviews were conducted using a guide informed by the Theoretical Domains Framework and Consolidated Framework for Implementation Research. Analysis was conducted using the Theoretical Domains Framework. RESULTS: Prominent domains identified by staff were "social influences and skills", "environmental context and resources", "social/professional role and identity", "emotion", and "belief of consequences". Prominent domains identified by safety coaches were "knowledge", "social/professional role and identity", "environmental context and resources", and "memory". Only "environmental context and resources" and "social/professional role and identity" were similar. The main facilitators were fear of COVID-19 and leadership commitment, while the main barriers were lack of clarity and balancing the role. DISCUSSION: Understanding the local context of a health care environment influenced the success of safety coaches. The role allowed individuals to develop leadership skills and help staff improve their perceived competence in using personal protective equipment. CONCLUSIONS: Safety coaches were well received. Influencing factors provide a basis for strategies to embed this approach throughout a health care system.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| 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".