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Record W234725119

Stress and burnout amongst Aborginal peoples : quantitative and qualitative inquiries

2004· article· en· W234725119 on OpenAlexaboutno aff
Robert G. Crow

Bibliographic record

VenueOpen ULeth Scholarship (OPUS) (University of Lethbridge) · 2004
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBurnoutPsychologyQualitative researchStress (linguistics)Social psychologyPolitical scienceSociologyClinical psychologySocial science
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this research project is to provide an understanding from a Aboriginal perspective of stress and burnout and how these phenomena exist within a Aboriginal worldview and environment.Using quantitative and qualitative methodologies, the research project seeks to identify the current burnout levels and occupational health of Aboriginal individuals and to compare these findings with the existing results from non-Aboriginal workers in Canada.To aid in understanding these results a narrative account of Aboriginal stress and burnout experiences is also provided.This research project is unique in that it seeks to provide an outlook about Aboriginal people from the perspective of Aboriginal people.The findings suggest that Aboriginal burnout levels identified in this study are comparable and of a similar virulence to those experienced by non-Aboriginal workers both in Canadian and global work settings.Over a third of employed Aboriginals participating in this study showed high levels of depersonalization, lack of personal accomplishment, and emotional exhaustion.The qualitative interviews also revealed several causes of stress that are unique to working in a Aboriginal environment.Taken together the results identify that Aboriginal stress and burnout are important issues that need to be addressed at the individual, organizational, and community levels.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.379
Teacher spread0.317 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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