DEMOGRAPHIC AND SOCIOECONOMIC FACTORS ASSOCIATED WITH ACCESS TO PUBLIC CLINICS
Bibliographic record
Abstract
Introduction: Providing adequate and equal access to healthcare is a key goal towards achieving universal health coverage. However, social and demographic inequalities in accessing health care services exist in both developed and developing countries. This study examined the demographic and socio-economic factors associated with the lack of access to public clinics in the Greater Kuala Lumpur area. Materials and Methods: The study employed a survey involving 1032 participants. Data were collected using self-administered questionnaires distributed between October and December 2015 in the Greater Kuala Lumpur area. Results: Of the 1032 participants, 535 were public clinic users. A quarter (25.8%) of the users stated that they did not have access to public clinics in their residential area. A multiple logistic analysis showed that the elderly, the women, those from ethnic minority groups, those of lower family income, and the private sector workers were more likely not to have access to public clinics than their counterparts. Conclusions: The existing level of accessibility to public clinics could be improved by increasing the number of clinics. Clinics should be established to focus more on reaching the elderly, the women, the ethnic minority groups, the poorer families, and the private sector employees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".