Understanding patient outcomes to develop a multimorbidity adapted patient-reported outcomes measure: a qualitative description of patient and provider perspectives
Bibliographic record
Abstract
BACKGROUND: Multimorbidity is a complex health situation that requires interventions tailored to patient needs; the outcomes of such interventions are difficult to evaluate. The purpose of this study was to describe the outcomes of patient-centred interventions for people with multimorbidity from the patients' and healthcare providers' perspectives. METHODS: This study followed a qualitative descriptive design. Nine patients with multimorbidity and 18 healthcare professionals (nurses, general practitioners, nutritionists, and physical and respiratory therapists), participating in a multimorbidity-adapted intervention in primary care were recruited. Data were collected using semi-structured interviews with 12 open-ended questions. Triangulation of disciplines among interviewers, research team debriefing, data saturation assessment and iterative data collection and analysis ensured a rigorous research process. RESULTS: Outcome constructs described by participants covered a wide range of themes and were grouped into seven outcome domains: Health Management, Physical Health, Functional Status, Psychosocial Health, Health-related Behaviours, General Health and Health Services. The description of constructs by stakeholders provides valuable insight on how outcomes are experienced and worded by patients. CONCLUSION: Participants described a wide range of outcome constructs, which were relevant to and observable by patients and were in line with the clinical reality. The description provides a portrait of multimorbidity-adapted intervention outcomes that are significant for the selection and development of clinical research outcome measures.
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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 | 0.038 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".