The Influence of Individuals’ Vulnerabilities and Their Interactions on the Assessment of a Primary Care Experience
Bibliographic record
Abstract
This study examines the relationship between the vulnerabilities of individuals and their assessments of their primary care experiences in the setting of a universal care system. It focuses on 2 specific objectives: (1) evaluating the influence of each of the 5 vulnerabilities on the assessment of the care experience; (2) evaluating the influence of the interactions between the different types of vulnerabilities on the assessment of the care experience. The study identifies the primary care experience of 9,206 people. The health-related, biological, material, relational, and cultural vulnerabilities are also evaluated. Generally, individuals' vulnerabilities are associated with a positive assessment of the primary care experience except for the cultural vulnerability. Material vulnerability is most frequently associated with a positive assessment of the primary care experience. The interactions between the multiple vulnerabilities present for one individual often modify the effect of vulnerability on the assessment of the experience of care. The positive effect of a vulnerability on the assessment of the care experience often increases in the presence of a second vulnerability, especially the health-related vulnerability. The simultaneous presence of health-related vulnerability cancels the negative influence of cultural vulnerability on the assessment of the primary care experience.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".