A survey of infection prevention and control resources in acute care facilities across British Columbia.
Bibliographic record
Abstract
BACKGROUND: To determine the gaps in infection prevention and control (IPAC) resources and the disparities between rural and urban areas, the Provincial Infection Control Network surveyed the current resources in British Columbia (BC). METHODS: Acute care facilities (ACF) in six health authorities (HA) were surveyed for IPAC staff; distribution of work; infection prevention and control professional (ICP) to bed ratios; and teaching activities. HAs were designated as either urban or rural. RESULTS: Responses represented 54 (68%) of the ACF in BC. Rural HAs showed a significantly higher number of inexperienced ICPs (68% vs. 17%; p < 0.001). Only 22 (60%) of eligible ICPs were Certification Board of Infection Control certified. Five out of six HAs (83%) reported having an IPAC physician. Acute care ICP to bed ratios ranged from one per 67 to one per 175 and combined acute and long-term care ICP to bed ratios ranged from one per 270 beds to one per 525 beds. The number of ICPs who reported working overtime on a consistent basis ranged from 20 to 100%. CONCLUSIONS: ACFs surveyed did not meet the recommended standards for staffing and IPAC resources in order to function as an effective program. Surveys of infection control resources are valuable tools to identify needs and assist in acquiring the resources to fill the identified gaps within a health authority.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".