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Record W4378086645 · doi:10.1177/21582440231177258

Exploring High School Students Career Interest in Aging for a Sustainable Workforce

2023· article· en· W4378086645 on OpenAlexaffabout
Suzanne Dupuis‐Blanchard, Danielle Thériault

Bibliographic record

VenueSAGE Open · 2023
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsWorkforceAging in the American workforcePopulation ageingSustainabilityPsychologyPopulationScarcityGerontologyPerceptionEconomic growthSociologyMedicineDemographyEconomics

Abstract

fetched live from OpenAlex

A scarcity of workers calls into doubt our capacity to support and care for an aging population. To gain a better understanding of the next generation of potential workers that will serve the aging population, 644 French-speaking students in grades 10 and 11 in the province of New Brunswick (Canada) were surveyed. The goals were to learn more about the attitudes, knowledge, and career interests that French-speaking, bilingual youth hold with respect to older adults. The participants’ responses indicate slightly positive attitudes toward older adults and little knowledge of this group. Results also showed that interest in a career working with older adults is primarily linked to holding positive attitudes toward them. By offering a deeper insight into young people’s perceptions of an aging population and contributing to a field lacking in research, the results of this study provide insight into measures that can be taken to ensure the future sustainability of the workforce for an aging population.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.390
GPT teacher head0.468
Teacher spread0.078 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2023
Admission routes2
Has abstractyes

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