Patient characteristics as predictors of primary health care preferences: a systematic literature analysis
Bibliographic record
Abstract
OBJECTIVE: To identify associations between various cultural and demographic factors and patients' primary health care preferences. SEARCH STRATEGY: Searches were performed in MEDLINE (1966-December 2000), PsycINFO (1977-May 2001) and Sociological Abstracts (1963-December 2000). Identified papers were checked for more papers. INCLUSION CRITERIA: Studies with a focus on primary health care or health care in general, asking patients about preferences with regard to health care, reporting quantitative results and examining the relations between specific patient characteristics and patient preferences. DATA EXTRACTION AND SYNTHESIS: Data were extracted from studies using a scoring form to register what methods were used, which patient characteristics were analysed and which patient characteristics significantly influenced patients' preferences with regard to different aspects of health care (P < 0.05). MAIN RESULTS: A total of 145 studies were included with 2276 comparisons between subgroups of patients. Of all the comparisons, 607 (27%) showed a significant association between patient characteristics and preferences with regard to primary health care. Age and economic status significantly related to patient preferences in 38 and 33% of the comparisons, respectively. Education, health status, family situation, sex, and utilization of health care related significantly to patient preferences in less than 25% of the comparisons. CONCLUSIONS: This review of the literature showed patient characteristics to be an important determinant of preferences regarding many aspects of primary health care defined as general practice care or health care, in general. All of the patient characteristics examined here showed at least some significant associations with preferences for primary health care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".