Understanding adolescent and young adult use of family physician services: a cross-sectional analysis of the Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: Primary health care is known to have positive effects on population health and may reduce at-risk behavior and health problems in adolescence. Yet little is known about the factors that are associated with adolescent and young adult utilization of family physician services. It is critical to determine the factors associated with utilization to inform effective primary health care policy. We address this gap in the primary health care literature by examining three issues concerning adolescent and young adult family physician use: inequity; the unique developmental stage of adolescence; and the distinction between utilization (users versus non-users) and intensity (high users versus low users). METHODS: We conducted nested logistic regressions for two outcomes: utilization and intensity of family physician services for early adolescence, middle adolescence, and young adulthood using the 2005 Canadian Community Health Survey. RESULTS: Chronic conditions were associated with utilization in early and middle adolescence and intensity in all age groups. Respondents from Quebec had lower odds of utilization. Those without a regular medical doctor had much lower odds of being users. The factors associated with use in early and middle adolescence were in keeping with parental involvement while the factors in young adulthood show the emerging independence of this group. CONCLUSIONS: We highlight key messages not known previously for adolescent and young adult use of family physician services. There is inequity concerning regional variation and for those who do not have a regular medical doctor. There is variation in factors associated with family physician services across the three age groups of adolescence. Health care and health care policies aimed at younger adolescents must consider that parents are still the primary decision-maker while older adolescents are more autonomous. There is variation in the factors associated with the two outcomes of utilization and intensity of services. Factors associated with utilization must be understood when considering the equitability of access to primary health care while factors associated with intensity must be understood when considering appropriate use of resources. The understanding gained from this study can inform health care policy that is responsive to the critical developmental stage of adolescence and young adulthood.
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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".