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Record W4393037062 · doi:10.1177/07334648241237099

Ageism Healthcare: Implications for the Psychological Well-Being of Atlantic Canadian Healthcare Professionals

2024· article· en· W4393037062 on OpenAlexaffabout
Madelyn Purchase, Éric R. Thériault, Brooke Collicutt

Bibliographic record

VenueJournal of Applied Gerontology · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsCape Breton UniversityDalhousie University
Fundersnot available
KeywordsCognitive dissonanceBurnoutPsychologyPsychological interventionHealth professionalsHealth careClinical psychologySocial psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Ageism in healthcare is related to poor outcomes for older patients. The objective of this study was to evaluate the relationships between ageism and various aspects of the psychological well-being of healthcare professionals in Atlantic Canada. In 2023, an online survey of 294 healthcare professionals from various disciplines was conducted. This survey included items to measure expectations of aging, stress, burnout, emotional dissonance, and well-being. Results indicated that aging expectations were significantly related to burnout, perceived stress, well-being, and emotional dissonance. With the use of a path analysis, emotional dissonance partially mediated relationships between burnout and well-being with stress. However, aging expectations did not significantly predict emotional dissonance. Differences were found across professional groups on ageism. Conclusions support the need for increased awareness to the relationship between ageist attitudes and professionals' well-being, as well as the need for education and interventions to reduce false expectations about the aging process.

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)0.001
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 score0.029
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.088
GPT teacher head0.475
Teacher spread0.387 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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