Availability and Accessibility of Primary Care for Community Dwelling Persons with Dementia
Bibliographic record
Abstract
This thesis comprises a description and analysis of access to primary care (PC) for community dwelling persons living with dementia (PLWD) in Ontario. PLWD benefit from PC for diagnosis, management, and connection to specialists and community supports, however, PC physicians and services are not equitably distributed in Ontario. The Patient Centred Accessibility Framework was used to define access and guide this thesis. Population-level health administrative databases were used to create a cross-section of community dwelling persons with prevalent, physician diagnosed dementia. To understand supply, demand, and realized access of community dwelling PLWD, the first objective used the cross-sectional study group described above, and semi-structured key informant interviews in a convergent mixed methods design. Access to PC in rural and urban areas was compared, finding that rural areas included a higher proportion of PLWD compared to urban areas, and that there was a larger supply of physicians in urban areas. Qualitatively, I explored how PC providers provide care and perceive supply and demand of health and community services for PLWD. Identified themes emphasized the different types of challenges in rural and urban settings. In objective two, I quantified the association between house calls (availability), travel time to PC physician (geographic accessibility), appropriateness (continuity of care) and emergency department (ED) use. I used regression analyses to measure the association in the study group, followed by stratification by urban and rural residence of PLWD. Results indicated that longer travel time to PC physician was associated with higher likelihood of ED use in urban settings. In the third objective, I measured the association between travel time, house calls, and continuity of care, and distress of caregivers of PLWD. Regression analyses of the entire study group were conducted, then the group was stratified by sex. Results demonstrated a positive association between having a physician who conducts house calls and caregiver distress. Findings from this thesis emphasize challenges in urban and rural access to PC, identifying potential for improving health use and outcomes among PLWD. These findings are relevant for clinicians, administrators, and policy makers working to improve access to PC in Ontario.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".