Youth perspectives on healthcare careers
Bibliographic record
Abstract
Recent literature suggests that experiential forms of learning are an effective means to promote healthcare careers as options for youth in secondary school, and that such efforts are especially important in recruiting youth from groups underrepresented in the healthcare workforce. Yet, little is known about how youth obtain information about career options, nor about the influence that contextual factors, such as rural or smaller population centres, have on the formation of career aspirations. This study reports onresults of an online survey questionnaire distributed to students in the mid-senior years of high school (i.e., Grade 10) at four high schools across British Columbia during the 2020-21 academic year. Students were generally aware of a range of healthcare career opportunities, but desired additional exposure and information. They obtained information about these career options from an array of sources (i.e., family, friends, teachers, popular culture, social media). Students in small centres appear to have more reservations about their own abilities to pursue healthcare careers. The cost of post-secondary education was the most cited barrier to pursuing a healthcare career across all populations and students suggested that costs should be lowered or eliminated to increase the number of youth working towards a career in healthcare. Finally, our data suggest that youth impressions about healthcare career planning andopportunities were largely unaffected by COVID-19, at least in the early stages of the pandemic, though students in small centres were more likely to be shy of a healthcare career due to the pandemic. In light of our findings, we offer recommendations to educators and policy makers
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".