Capturing the experiences of patients across multiple complex interventions: a meta-qualitative approach
Bibliographic record
Abstract
OBJECTIVES: The perspectives, needs and preferences of individuals with complex health and social needs can be overlooked in the design of healthcare interventions. This study was designed to provide new insights on patient perspectives drawing from the qualitative evaluation of 5 complex healthcare interventions. SETTING: Patients and their caregivers were recruited from 5 interventions based in primary, hospital and community care in Ontario, Canada. PARTICIPANTS: We included 62 interviews from 44 patients and 18 non-clinical caregivers. INTERVENTION: Our team analysed the transcripts from 5 distinct projects. This approach to qualitative meta-evaluation identifies common issues described by a diverse group of patients, therefore providing potential insights into systems issues. OUTCOME MEASURES: This study is a secondary analysis of qualitative data; therefore, no outcome measures were identified. RESULTS: We identified 5 broad themes that capture the patients' experience and highlight issues that might not be adequately addressed in complex interventions. In our study, we found that: (1) the emergency department is the unavoidable point of care; (2) patients and caregivers are part of complex and variable family systems; (3) non-medical issues mediate patients' experiences of health and healthcare delivery; (4) the unanticipated consequences of complex healthcare interventions are often the most valuable; and (5) patient experiences are shaped by the healthcare discourses on medically complex patients. CONCLUSIONS: Our findings suggest that key assumptions about patients that inform intervention design need to be made explicit in order to build capacity to better understand and support patients with multiple chronic diseases. Across many health systems internationally, multiple models are being implemented simultaneously that may have shared features and target similar patients, and a qualitative meta-evaluation approach, thus offers an opportunity for cumulative learning at a system level in addition to informing intervention design and modification.
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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 | 0.202 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".