Prioritization of indicators of the quality of care provided to older adults with frailty by key stakeholders from five canadian provinces
Bibliographic record
Abstract
BACKGROUND: To meet the needs of older adults with frailty better, it is essential to understand which aspects of care are important from their perspective. We therefore sought to assess the importance of a set of quality indicators (QI) for monitoring outcomes in this population. METHODS: In this mixed-method study, key stakeholders completed a survey on the importance of 36 QIs, and then explained their ratings in a semi-structured interview. Stakeholders included older adults with frailty and their caregivers, healthcare providers (HCPs), and healthcare administrators or policy/decision makers (DMs). We conducted descriptive statistical analyses of quantitative variables, and deductive thematic qualitative analyses of interview transcripts. RESULTS: The 42 participants (8 older adults, 18 HCPs, and 16 DMs) rated six QIs as more important: increasing the patients' quality of life; increasing healthcare staff skills; decreasing patients' symptoms; decreasing family caregiver burden; increasing patients' satisfaction with care; and increasing family doctor continuity of care. CONCLUSIONS: Key stakeholders prioritized QIs that focus on outcomes targeted to patients and caregivers, whereas the current healthcare systems generally focus on processes of care. Quality improvement initiatives should therefore take better account of aspects of care that are important for older adults with frailty, such as having a chance to express their individual goals of care, receiving quality communications from HCPs, or monitoring symptoms that they might not spontaneously describe. Our results point to the need for patient-centred care that is oriented toward quality of life for older adults with frailty.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".