1.M. Oral presentations: Efficacy in health care
Bibliographic record
Abstract
BackgroundThis study evaluates whether contracting out cleaning services in English acute hospital Trusts is associated with risks of hospital-borne MRSA infection and lower economic costs. MethodsWe linked data on MRSA incidence per 100,000 hospital beddays with patient and staff surveys of cleanliness, for 126 English acute hospital Trusts, covering 2010-2014.Using as our main outcomes the MRSA incidence rate per 100,000 hospital bed-days and the average cost of cleaning per hospital, we estimate multi-variate regression models adjusting for hospital size, complexity of service provision, patient mix, and other potential confounding factors.As a sensitivity test we also re-estimate our models using propensity score matching. ResultsThe MRSA incidence rate was 15.3% higher (95% CI: 8.80% to 19.9%) in acute Trusts which outsourced hospital cleaning services compared to those with in-house cleaning, after adjusting for complexity of service provision and hospital size, and when using propensity score matching.Outsourcing was associated with fewer cleaning-personnel per hospital bed (5.86%; 95% CI: -7.88% to -1.90%), lower percentage of patients reporting excellent cleanliness for both bathrooms (-0.90 percentage points; 95% CI: -1.94 to -0.14 percentage points) and rooms/wards (-1.28 percentage points; 95 CI: -2.08 to -0.43 percentage points), and lower percentage staff reports that hand-washing material is always available (2.27 percentage points; 95% CI: -3.40 to -1.44 percentage points).Yet outsourcing was also associated with lower economic costs of about 6.93% per hospital bed (95% CI: -9.37% to -4.60%), corresponding to savings of £214 per bed-year. ConclusionsOutsourcing cleaning services was associated with greater risks of MRSA, fewer cleaning staff per hospital bed, worse patient perceptions of cleanliness and staff perceptions of availability of sanitary materials.However, outsourcing was also associated with lower economic costs.
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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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".