Career aspirations and trajectories of geographies of health and health care graduates: A cross‐sectional study
Bibliographic record
Abstract
Despite the diverse academic training of health geographers and their capacity to think spatially and temporally when focusing on complexities of people's health in changing environments, career‐related concerns have been raised due to the dearth of academic jobs in the sub‐discipline. Given these concerns, an understanding of the career aspirations and trajectories of health geography graduates, as well as their experiences within and outside academia, has the potential to inform the progression and evolution of the sub‐discipline. This cross‐sectional study of members of the Geography of Health and Health Care Specialty Group of the Canadian Association of Geographers (n = 56) found that acquiring academic work remains the desired career goal for many geography graduates. While a majority of participants aspired to work in academia, some participants reported insecurity in obtaining academic jobs, and precarity in this trajectory. Although belonging to the specialty group has contributed positively to the experiences of graduates in providing network opportunities and job positions, there were calls for critical engagement of the group in mentorship, networking, and increasing exposure to careers outside of academia to help inform the future direction and the inter‐relationship of academics within the sub‐discipline of health geography.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".