Factors associated with primary health care contacts by the elderly population in groups of family doctors in Quebec, Canada
Bibliographic record
Abstract
INTRODUCTION The aging population in Quebec, combined with the chronic disease rise, has increased the health care service use among the elderly population. Therefore, elderly care has largely relied on primary health care (PHC) providers as they are best positioned to care for such population. This influx of PHC physician contacts, both face-to-face and virtual, has become a concern due to the limited PHC physician resources. As such, a clear understanding of the factors contributing to PHC contacts by the elderly population is needed. OBJECTIVES To identify the factors contributing to the number of PHC contacts by the elderly population in Quebec family medicine groups, or Groupes de Medecine de Famille (GMF). METHODS In a cross-sectional design, two main data sources were used: 1) A chart review from the Alzheimer's Plan Evaluation Study provided patient-level factors and the number of PHC contacts. 2) The Quebec Ministry of Health information pertaining to GMF-level factors. A total of 1,919 patients were randomly selected. Eligibility criteria included patients aged 75+ years with a minimum of one PHC contact in a 9-month period. Descriptive analyses of independent variables and the study outcome were performed. Generalized Estimating Equations; GEE models were used to analyze correlated data with binary, discrete, or continuous outcomes. RESULTS Descriptive results:Males represented 40% (768 patients) of the study population. Patient age ranged from 75.0 to 104.0 (mean=81.7, SD=5.0) years. Patients aged 75.0-79.9, 80.0-84.9, and 85+ represented 44.1%, 30.5%, and 25.4% of the population, respectively. Nearly half (49.7%) lived with the family, whereas 20% lived alone. A total of 22,221 medications were retrieved from patient charts to identify chronic diseases. Of those medications, 16,336 were matched to 21 chronic diseases. The number of chronic and non-chronic disease medications ranged from 0 to 33 (mean=8.5, SD=5.3) and 0 to 17 (mean=3.0, SD=2.5) respectively. The number of chronic diseases identified ranged from 0 to 17 (mean=5.7, SD=2.9).Elderly proportion among total registered patients ranged from 7% to 17% (mean=12.1%, SD=3.4%). The number of patients per Full-Time Equivalent (FTE)-physician and FTE-RN ranged from 816 to 2,115 (mean=1,244, SD=439) and from 3,218 to 14,193 (mean 8,048.9, SD=3,909.5), respectively. The number of sites within GMFs ranged from 1 to 8 (mean=3.25, SD=2.5). GMF years of operation ranged from 2.2 to 11 years (mean=7.6, SD=3.0). In terms of the study outcome, total PHC contacts ranged from 1 to 81 (mean=4.4, SD=5.1).GEE results:The "oldest old‟ population group (85+) showed a statistically significant 16.4% increase in PHC contact incidence. Likewise, each additional chronic disease showed an 11% increase in the incidence of PHC contacts. The proportion of elderly population showed a 4.5% decrease in PHC contact incidence for each additional 1% of elderly patients. The number of physicians per FTE physician had a 1.6% decrease in the PHC contact incidence for each additional physician. Moreover, Université Laval-affiliated GMF sites had a 60% higher PHC contact incidence compared to Université de Sherbrooke, our reference (p=0.001). Likewise, public GMFs had an 18.2% lower PHC contact incidence than mixed GMFs. CONCLUSION This study provides an evidence-based description of the delivery of PHC contacts among the elderly. Study findings can guide GMF managers and health policy makers, and assist in the development of well-informed staffing, budgetary plans, and decisions in Quebec.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".