Barriers, Enablers, and Impacts of Implementing National Comprehensive Care Standards in Acute Care Hospitals: An Interview Study
Bibliographic record
Abstract
Background: Comprehensive care is increasingly being recognised as a critical component of healthcare, with several countries endorsing it as a national standard. This study aims to explore care professionals’ perspectives on the barriers, enablers, and impacts of implementing the Comprehensive Care Standard (CCS) in acute care hospitals across Australia. Methods: This is a qualitative descriptive study. Participants included 28 care professionals (20 nurses, 2 doctors, and 6 allied health professionals) recruited from a broad range of Australian acute care hospitals. Data were collected using semi-structured interviews from March to August 2023. The interviews were audio-recorded, transcribed and thematically analysed. Data collection and analysis were guided by the Consolidated Framework for Implementation Research (CFIR), and implementation strategies were mapped to the Expert Recommendations for Implementing Change (ERIC). Results: CFIR-informed analysis identified 12 barriers and 13 enablers to CCS implementation, most prominently within the Inner Setting and Implementation Process domains. Sixteen implementation strategies were also mapped using the CFIR-ERIC Mapping Tool. The perceived impacts of the CCS implementation were multifaceted. While CCS implementation brought about changes to hospitals and improvements in patient care, it also resulted in increased workload and fatigue among staff. Conclusions: Enhancing CCS implementation will involve addressing the barriers and building on the enablers identified in this study. Supporting more effective implementation may help maximise the benefits of the CCS for patient care while also mitigating the increased workload and fatigue reported by staff. These findings highlight the importance of approaches that balance quality improvements with staff wellbeing.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".