Patient preferences for key organizational features of primary cardiovascular care in Quebec: a discrete choice experiment
Bibliographic record
Abstract
BACKGROUND: Cardiovascular diseases and their risk factors are leading causes of morbidity and mortality worldwide, and are among the top reasons for primary care visits. Little is known about patient preferences for primary care in the context of chronic conditions. This study aimed to investigate the effect of key organizational features identified by patients and providers on patients' choice of a preferred primary care practice to receive cardiovascular care. METHODS: A discrete choice experiment survey was completed by a weighted online sample of 501 Quebec residents having or being at risk of cardiovascular disease. Respondents completed one of two blocks of nine choice sets by indicating, among three hypothetical primary care practice alternatives in each choice set, their preferred and second-most preferred options. Alternatives were differentiated on the basis of five key attributes identified as priorities in an earlier Delphi study: listening to and respecting care preferences; providing personalized information; 24-to- 48-h accessibility in the event of a problem; continuity of care; and up-to-date clinical skills. Each attribute could be assigned a best, moderate, or worst level. Choices were analyzed using generalized multinomial logit modeling. Marginal effects and choice probabilities for policy-relevant scenarios were estimated. RESULTS: All five attributes significantly influenced choices of primary care practice. The marginal effects of worst attribute levels were of much greater magnitude than those of best levels for all attributes. Improving short-term accessibility from worst to moderate level had the largest average incremental effect on the probability of patients choosing a practice. Best continuity of care was more valued by older patients and those in poorer general health, but had nonsignificant impact unless it was coupled with enhanced short-term accessibility. CONCLUSIONS: A balanced approach across the key organizational features covered seems more advantageous for primary care practices than focusing solely on achieving excellence in any single attribute. The interactions between patient preferences for short-term accessibility and continuity of care should be taken into account when planning and implementing organizational change in primary care. Whether these preferences are generalizable to other jurisdictions and subsets of primary care patients deserves further exploration.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".