Bibliographic record
Abstract
The study examines the difference in characteristics between primary care patients who turn to “religious resources for medical purposes” (RRMP) and those who turn to “complementary or alternative medicine” (CAM) services to cope with a physical or mental health problem.Data were collected from eight primary care clinics in Israel and included 905 Jewish patients aged 25–75.A self-report questionnaire with a battery of validated mental health assessment instruments and two questionnaires regarding use of unconventional therapies (RRMP and CAM services) were administered to the participants. The association of various variables with type of ‘service use’ was examined through logistic regression analysis.Primary care patients suffering from emotional problems have a propensity to utilize unconventional therapies in addition to conventional medical treatment. However, differences exist between patients who turn to RRMP and to CAM. The risk factors for turning to RRMP are North African, Middle Eastern or Israeli origin, low SES, religious observance, and high use of primary care clinics. For using CAM services the risk factor is high SES.In the present study, a quarter of primary care patients also use additional resources for their medical problems. While all segments of the population use unconventional resources, our study reveals that two types of unconventional therapies – RRMP and CAM – tend to be used by two different population sectors. It is noteworthy that those suffering from mental health problems are more likely to utilize unconventional resources.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.001 |
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".