At the interface of community and healthcare systems: a longitudinal cohort study on evolving health and the impact of primary healthcare from the patient's perspectiv
Bibliographic record
Abstract
BACKGROUND: Massive efforts in Canada have been made to renew primary healthcare. However, although early evaluations of initiatives and research on certain aspects of the reform are promising, none have examined the link between patient assessments of care and health outcomes or the impacts at a population level. The goal of this project is to examine the effect of patient-centred and effective primary healthcare on the evolution of chronic illness burden and health functioning in a population, and in particularly vulnerable groups: the multi-morbid and the poor. METHODS/DESIGN: A randomly selected cohort of 2000 adults aged 25 to 75 years will be recruited within the geographic boundaries of four local healthcare networks in Quebec. At recruitment, cohort members will report on socio-demographic information, functional health and healthcare use. Two weeks, 12 months and 24 months after recruitment, cohort participants will complete a self-administered questionnaire on current health and health behaviours in order to evaluate primary healthcare received in the previous year.The dependent variables are calculated as change over time of functional health status, chronic illness burden, and health behaviours. Dimensions of patient-centred care and clinical processes are measured using sub-scales of validated instruments. We will use Poisson regression modelling to estimate the incidence rate of chronic illness burden scores and structural equation modelling to explore relationships between variables and to examine the impact of dimensions of patient-centred care and effective primary healthcare. DISCUSSION: Results will provide valuable information for primary healthcare clinicians on the course of chronic illness over time and the impact on health outcomes of accessible, patient-centred and effective care. A demonstration of impact will contribute to the promotion of continuous quality improvement activities at a clinical level. While considerable advances have been made in the management of specific chronic illnesses, this will make a unique contribution to effective care for persons with multiple morbidities. Furthermore, the cohort and data architecture will serve as a research platform for future projects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".