A New Approach to Improving Healthcare Personnel Influenza Immunization Programs: A Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Healthcare personnel influenza immunization rates remain sub-optimal. Following multiple studies and expert consultations, the "Successful Influenza Immunization Programs for Healthcare Personnel: A Guide for Program Planners" was produced. This trial assessed the impact of the Guide with facilitation in improving healthcare personnel influenza immunization rates in Canadian healthcare organizations. METHODS: A sample of 26 healthcare organizations across six Canadian provinces (ON, MB, NS, BC, SK, NL) was randomized to Intervention (n=13) or Control groups (n=13). Baseline influenza immunization rates were obtained for 2008-2009; the study groups were followed over two subsequent influenza seasons. The Intervention group received the Guide, facilitation support through workshops for managers and ongoing support. The Control groups conducted programs as usual. The Groups were compared using their reported influenza healthcare personnel influenza immunization rates and scores from a program assessment questionnaire. FINDINGS: Twenty-six organizations agreed to participate. 35% (9/26) of sites were acute care hospitals, 19% (5/26) continuing care, long-term care organizations or nursing homes, and 46% (12/26) were mixed acute care hospitals and long-term care or regional health authorities. The median rate of influenza immunization among healthcare personnel for the Intervention group was 43%, 44%, and 51% at three points in time respectively, and in the CONTROL GROUP: 62%, 57%, and 55% respectively. No significant differences were observed between the groups at the three points in time. However, there was a 7% increase in the median rates between the Baseline Year and Year Two in the Intervention group, and a 6% decrease in the CONTROL GROUP over the same time period, which was statistically significant (0.071 versus -0.058, p < 0.001). INTERPRETATION: This pragmatic randomized trial of the Guide with facilitation of its implementation improved healthcare personnel immunization rates, but these rates continued to be sub-optimal and below rates achievable in programs requiring personnel to be immunized. TRIAL REGISTRATION: ClinicalTrials.gov NCT01207518.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".