Support for teams, technology and patient involvement in decision-making associated with support for patient-centred care
Bibliographic record
Abstract
OBJECTIVE: Patient-centred care is recommended to transform healthcare delivery to improve the quality and safety of healthcare. This study aimed to assess the determinants of support for attributes of patient-centred care (PCC) from Canadian public and professionals' perspectives. DESIGN: A national population-based survey, the Health Care in Canada Survey. SETTING: Canada. PARTICIPANTS: One-thousand Canadian adults, 101 doctors, 100 nurses, 100 pharmacists and 104 administrators, randomly selected from online panels based on multiple source recruitment. INTERVENTION: None. MAIN OUTCOME MEASURE: Support for PCC, assessed using a summary score across seven items. RESULTS: Of 1000 Canadian public adults surveyed, 51% were female, 74% were living with another person, and 62% had at least one chronic condition. Only 18% of health professionals were working in teams. Multivariable regression models showed that work in teams (0.24, 95%CI: 0.20, 0.28), use of e-technology (0.29, 95%CI: 0.17, 0.42), and patient older age (0.59, 95%CI: 0.32, 0.86) and involvement in decision-making (0.42, 95%CI: 0.30, 0.55) were significantly associated with higher support for PCC while lower adherence to medications (-0.81, 95%CI: -1.16, -0.47) was associated with a decreased support for attributes of PCC. CONCLUSIONS: The findings confirmed that perceptions of requiring health professionals to work in teams and the use of technology in healthcare are associated with support for PCC from both the public and health professionals. Programs to accelerate the implementation of healthcare teams supported by information and communication technologies are needed to deliver PCC, particularly for individuals living with chronic conditions.
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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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".