IMpleMenting Effective infection prevention and control in ReSidential aged carE (IMMERSE): protocol for a multi-level mixed methods implementation study
Bibliographic record
Abstract
BACKGROUND: Older people living in residential aged care facilities are at high risk of acquiring infections such as influenza, gastroenteritis, and more recently COVID-19. These infections are a major cause of morbidity and mortality among this cohort. Quality infection prevention and control practice in residential aged care is therefore imperative. Although appointment of a dedicated infection prevention and control (IPC) lead in every Australian residential aged care facility is now mandated, all people working in this setting have a role to play in IPC. The COVID-19 pandemic revealed inadequacies in IPC in this sector and highlighted the need for interventions to improve implementation of best practice. METHODS: Using mixed methods, this four-phase implementation study will use theory-informed approaches to: (1) assess residential aged care facilities' readiness for IPC practice change, (2) explore current practice using scenario-based assessments, (3) investigate barriers to best practice IPC, and (4) determine and evaluate feasible and locally tailored solutions to overcome the identified barriers. IPC leads will be upskilled and supported to operationalise the selected solutions. Staff working in residential aged care facilities, residents and their families will be recruited for participation in surveys and semi-structured interviews. Data will be analysed and triangulated at each phase, with findings informing the subsequent phases. Stakeholder groups at each facility and the IMMERSE project's Reference Group will contribute to the interpretation of findings at each phase of the project. DISCUSSION: This multi-site study will comprehensively explore infection prevention and control practices in residential aged care. It will inform and support locally appropriate evidence-based strategies for enhancing infection prevention and control practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".