Patient Activation in Primary Healthcare
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Current policy directions place increasing expectations on patients to actively engage in their care, especially in chronic disease management. We examined relationships between patient activation and multiple dimensions of primary healthcare (PHC), including access, utilization, responsiveness, interpersonal communication, and satisfaction for patients with and without chronic illness. RESEARCH DESIGN: Cross-sectional, random digit dial survey conducted in British Columbia (BC), Canada. SUBJECTS: Stratified sample of adults (n=504), aged 19 to 90 years, who had visited their regular provider within the past 24 months. All data were weighted to represent residents living in BC. MEASURES: Patient activation and PHC experiences include accessibility, continuity, whole-person care, interpersonal communication, responsiveness, chronic disease management, and satisfaction. RESULTS: The multivariate models provide evidence that both quantity of time and quality of interactions with one's regular provider are associated with higher patient activation. Those with no chronic illness had higher activation scores when they spent more time talking with their regular provider, experienced less hurried communication, or if their test results were explained. The more time people with chronic illness are able to spend with their physician, the more activated they were. Chronic illness respondents also had higher activation scores if they reported higher whole-person care or if they were more satisfied. CONCLUSIONS: Positive interactions between the patient and the provider can influence the patient's abilities to engage in and be confident in maintaining/improving his/her health. Supporting patients in becoming actively engaged, in ways that work for them, is essential to providing high quality care, especially among those with a chronic condition.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".