Factors Influencing the Pursuit of Health and Science Careers for Canadian Adolescents in Transition from School to Work
Bibliographic record
Abstract
Previous research separately acknowledges two emerging trends in adolescence but neglects to integrate them. These are that many changes have occurred in the school to work transitional processes, and that there is substantial need for adolescents, especially young women, to pursue science career pathways. In this study, we link these trends and develop predictive, interactive models of science pursuit for 836 Canadian secondary school graduates living through a period of massive change in school to work transitional processes. Separate logit analyses were conducted for males and females. Results suggest that young women are not under-represented in the pursuit of science careers in high school. Young women aspire more frequently to medical and health sciences, and young men to natural sciences, engineering and mathematics. For young women, father's occupation in science, curriculum track and level of occupational expectation were significant in the model, correctly predicting 72% of membership in science. For males, socioeconomic status, family support, level of occupational expectation, regional unemployment levels and items measuring work environment were significant in the model that predicted 81% of membership in science. The findings suggest the salience of gender-differentiated school to work transition models in determining pursuit of health and science career pathways.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".