Which Experiences of Health Care Delivery Matter to Service Users and Why? A Critical Interpretive Synthesis and Conceptual Map
Bibliographic record
Abstract
OBJECTIVE: Patients' experiences are often treated as health care quality indicators. Our aim was to identify the range of experiences of health care delivery that matter to patients and to produce a conceptual map to facilitate consideration of why they matter. METHODS: Broad-based review and critical interpretive synthesis of research literature on patients' perspectives of health care delivery. We recorded experiences reported by a diverse range of patients on 'concept cards', considered why they were important, and explored various ways of organizing them, including internationally recognized health care quality frameworks. We developed a conceptual map that we refined with feedback from stakeholders. RESULTS: Patients identify many health care experiences as important. Existing health care quality frameworks do not cover them all. Our conceptual map presents a rich array of experiences, including health care relationships (beyond communication) and their implications for people's valued capabilities (e.g. to feel respected, contribute to their care, experience reciprocity). It is organized to reflect our synthesis argument, which links health care delivery to what people are enabled (or not) to feel, be and do. The map highlights the broad implications of the social dynamics of health care delivery. Experiences are labelled from a patient's perspective, rendering the importance of responsiveness to individuals axiomatic. CONCLUSIONS: Our conceptual map identifies and helps explain the importance of diverse experiences of health care delivery. It challenges and helps policy-makers, service providers and researchers to attend to the range of experiences that matter, and to take seriously the need for responsiveness to individuals.
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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 | 0.153 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".