Decisional needs assessment of patients with complex care needs in primary care
Bibliographic record
Abstract
RATIONALE: Patients with complex care needs who frequently use health services often face challenges in managing their health and with integrated care, leading to frequent decision making. These complex care needs require a good understanding of health issues and their impact on daily life. As the decisional needs of this particular clientele have yet to be described in scientific literature, they warrant further study. OBJECTIVES: To assess the decision-making needs of patients with complex care needs (PCCN) who frequently use health care services. METHODS: We performed a multicenter cross-sectional qualitative descriptive study in four institutions of the health and social services network of Quebec (Canada). We enrolled a convenience sample of PCCNs who frequently use health care services, health care providers, case managers, and decision-makers. We conducted interviews and focus groups and investigated decisional needs according to the Ottawa decision support framework: roles played and desired in the decision-making process, facilitators, and barriers. We conducted qualitative data collection and qualitative deductive/inductive thematic analysis within and across participating groups. RESULTS: In total, 16 patients, 38 clinicians, six case managers, and 14 decision-makers participated in the study. The decisional needs of this clientele are numerous, varied and different from those of the general population. We identified 26 decisional needs grouped under five themes. The most frequent decisions related to visiting the emergency department, moving to a nursing home, and adhering to a plan or treatment. In addition, we identified new themes such as patients' fear and mistrust of health professionals, differences of opinion between health professionals and health professionals' preconceived opinions of patients. CONCLUSION: We observed a wide range of types of decisions that patients face and differences in decision-making needs across participating groups. Our results should inform future research on the development of a patient decision aid tool.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".