Identification of implementation enhancement strategies for national comprehensive care standards using the CFIR-ERIC approach: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Comprehensive care is important for ensuring patients receive coordinated delivery of healthcare that aligns with their needs and preferences. While comprehensive care programs are recognised as beneficial, optimal implementation strategies in the real world remain unclear. This study utilises existing implementation theory to investigate barriers and enablers to implementing the Australian National Safety and Quality Health Service Standard 5 - Comprehensive Care Standard in acute care hospitals. The aim is to develop implementation enhancement strategies for work with comprehensive care standards in acute care. METHODS: Free text data from 256 survey participants, who were care professionals working in acute care hospitals across Australia, were coded using the Consolidated Framework for Implementation Research (CFIR) using deductive content analysis. Codes were then converted to barrier and enabler statements and themes using inductive theme analysis approach. Subsequently, CFIR barriers and enablers were mapped to the Expert Recommendations for Implementing Change (ERIC) using the CFIR-ERIC Matching Tool, facilitating the development of implementation enhancement strategies. RESULTS: Twelve (n = 12) CFIR barriers and 10 enablers were identified, with 14 barrier statements condensed into 12 themes and 11 enabler statements streamlined into 10 themes. Common themes of barriers include impact of COVID-19 pandemic; heavy workload; staff shortage, lack of skilled staff and high staff turnover; poorly integrated documentation system; staff lacking availability, capability, and motivation; lack of resources; lack of education and training; culture of nursing dependency; competing priorities; absence of tailored straties; insufficient planning and adjustment; and lack of multidisciplinary collaboration. Common themes of enablers include leadership from CCS committees and working groups; integrated documentation systems; established communication channels; access to education, training and information; available resources; culture of patient-centeredness; consumer representation on committees and working groups; engaging consumers in implementation and in care planning and delivery; implementing changes incrementally with a well-defined plan; and regularly collecting and discussing feedback. Following the mapping of CFIR enablers and barriers to the ERIC tool, 15 enhancement strategies were identified. CONCLUSION: This study identified barriers, enablers, and recommended strategies associated with implementing a national standard for comprehensive care in Australian acute care hospitals. Understanding and addressing these challenges and strategies is not only crucial for the Australian healthcare landscape but also holds significance for the broader international community that is striving to advance comprehensive care.
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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 | 0.023 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".