Narrowing the policy gap: lessons from years 2 and 3 of the British Columbia influenza prevention policy
Bibliographic record
Abstract
Influenza can be potentially fatal to vulnerable populations, particularly those in the hospital. Canada's National Advisory Committee on Immunization recommends that health-care workers (HCW) be immunized against influenza partly to avoid infecting high-risk populations. However, influenza immunization rates among HCW remain suboptimal. In 2012, health authorities across British Columbia (B.C.) implemented a province-wide influenza prevention policy requiring HCW to either be immunized or wear a mask when in patient-care areas during the influenza season. This paper describes the second of two studies focused on what was learned from years 2 and 3 of the policy. A case study approach was used to examine this policy implementation event. Qualitative data were collected through key documents and key informant interviews with members of leadership teams responsible for policy implementation. Framework analysis and Prior's approach were used to analyze data from interviews and documents, respectively. Policy implementation varied by geographic region and gaps persist in immunization tracking and discipline for noncompliance. Debate regarding the scientific evidence used to support the policy fuels resistance from particular groups. Despite these challenges, findings suggest that the policy has been habituated, largely due to consistent policy objectives. This study emphasizes the importance of ongoing inter-professional and cross-sectoral program evaluation. While adherence may be routine for many, implementation processes must continue to respond to contextual issues to narrow the gap in policy implementation and to continue to engage stakeholders to ensure compliance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.013 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 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".