An evaluation of audiology service improvement documentation in England using the chronic care model and content analysis
Bibliographic record
Abstract
OBJECTIVE: Implementation of the chronic care model (CCM) is associated with improved outcomes for patients. It follows that any proposed policy or implementation plan that maps highly onto the CCM is more likely to lead to improved outcomes. The aim of this study was to compare long-term condition (LTC) policy documents and audiology quality standard documents with the CCM and to highlight the need for further research in service implementation and clinical outcome. DESIGN: We carried out a keyword-in-context content analysis of relevant documents. STUDY SAMPLE: Documents relating to health department policy on LTCs, audiology service improvement initiatives in England and the CCM. RESULTS: This analysis shows that current audiology implementation documents in England map poorly onto the CCM compared to health policy documents relating to the management of LTCs. The biggest discrepancies occur in self-management support, delivery system design, and decision support. These elements are supported by the best evidence of potential improvements in clinical outcome. CONCLUSIONS: Our content analysis of audiology service quality improvement documents in England suggests they compare poorly to some elements of the CCM. We discuss the implications this might have for future research.
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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 | 0.068 | 0.195 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".