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Record W7127165546 · doi:10.18357/wg25202316

Youth perspectives on healthcare careers

2023· article· W7127165546 on OpenAlexaff
Sean B Maurice, Alishia Lindsay, Neil Hanlon

Bibliographic record

VenueWestern Geography · 2023
Typearticle
Language
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsHealth careExperiential learningCareer PathwaysCareer developmentPopulationCareer planning

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.040
GPT teacher head0.300
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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