Successful strategies for high participation in three regional healthcare surveys: an observational study
Bibliographic record
Abstract
BACKGROUND: Regional healthcare facility surveys to quantitatively assess nosocomial infection rates are important for confirming standardized data collection and assessing health outcomes in the era of mandatory reporting. This is particularly important for the assessment of infection control policies and healthcare associated infection rates among hospitals. However, the success of such surveys depends upon high participation and representativeness of respondents. METHODS: This descriptive paper provides methodologies that may have contributed to high participation in a series of administrative, infection control, and microbiology laboratory surveys of all 31 hospitals in a large southern California county. We also report 85% (N = 72) countywide participation in an administrative survey among nursing homes in this same area. RESULTS: Using in-person recruitment, 48% of hospitals and nursing homes were recruited within one quarter, with 75% recruited within three quarters. CONCLUSIONS: Potentially useful strategies for successful recruitment included in-person recruitment, partnership with the local public health department, assurance of anonymity when presenting survey results, and provision of staff labor for the completion of detailed survey tables on the rates of healthcare associated pathogens. Data collection assistance was provided for three-fourths of surveys. High compliance quantitative regional surveys require substantial recruitment time and study staff support for high participation.
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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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".