MétaCan
Menu
Back to cohort
Record W4401431431 · doi:10.1177/10482911241267347

Running on Empty: Ontario Hospital Workers’ Mental Health and Well-Being Deteriorating Under Austerity-Driven System

2024· article· en· W4401431431 on OpenAlexaffabout
James T. Brophy, Margaret M. Keith, Michael Hurley, Craig Slatin

Bibliographic record

VenueNEW SOLUTIONS A Journal of Environmental and Occupational Health Policy · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsAthabasca UniversityUniversity of Windsor
Fundersnot available
KeywordsStaffingMental healthAusterityRestructuringHealth careLegislationNursingThematic analysisBurnoutOccupational safety and healthPopulationMedicinePublic healthBusinessPsychologyPolitical scienceEnvironmental healthPsychiatryQualitative researchSociologyFinance

Abstract

fetched live from OpenAlex

The well-being of health care workers (HCWs) and the public in Ontario, Canada is at risk as the province's health care system is strained by neoliberal restructuring and an aging population. Deteriorating working conditions that preceded the COVID-19 pandemic further declined as the added challenges took their toll on the work force, physically and mentally. The pandemic-weary hospital staff, predominantly women, many racialized, are facing unprecedented challenges. They are experiencing stress, anxiety, and burnout from staffing shortages and the resulting increased workloads, long hours, and violence. Comprehensive telephone interviews were conducted with 26 HCWs from less highly paid occupations in a range of hospitals across the province. Thematic analysis reveals a critical need for policies and legislation ensuring increased funding, hospital capacity, and reduced wait times while providing HCWs with fair and equitable wages, increased staffing, mental health supports, greater respect and acknowledgment, and strong protections from violence and other workplace hazards.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.999

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 studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.047
GPT teacher head0.402
Teacher spread0.354 · 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 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

Citations6
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueNEW SOLUTIONS A Journal of Environmental and Occupational Health PolicySame topicHealthcare professionals’ stress and burnoutFrench-language works237,207