Predictors of adolescent health care utilization
Bibliographic record
Abstract
This study, using Andersen's health care utilization model, examined how predisposing characteristics, enabling resources, need, personal health practices, and psychological factors influence health care utilization using a nationally representative, longitudinal sample of Canadian adolescents. Second, this study examined whether this process varies across physicians, non-physicians, and dentists. The results indicate that need and psychological factors were strong determinants of utilization. Predisposing factors were associated with utilization, although there were few enabling resources. Differences were found for utilization of different services. Females, adolescents who were older, from single parent families, with lower self-rated health, lower health status, higher disability, higher distress and involved in health compromizing practices were more likely to visit physicians and non-physicians. Higher dentist utilization was related to higher income, single parent status, being younger, having lower health status, and higher disability. Predisposing and enabling factors were not mediators of utilization. The findings suggest that health care providers could be an important source of counselling on psychological, lifestyle issues, and physical concerns.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".