MétaCan
Menu
Back to cohort
Record W4281254118 · doi:10.1177/08404704221090087

Caring for caregivers: Supporting the psychological health and well-being of healthcare workers

2022· article· en· W4281254118 on OpenAlexaffabout
Ed Mantler

Bibliographic record

VenueHealthcare Management Forum · 2022
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsMental Health Commission of Canada
Fundersnot available
KeywordsHealth careStressorPandemicMental healthStigma (botany)Context (archaeology)PsychologyNursingPsychological resilienceResilience (materials science)Coronavirus disease 2019 (COVID-19)MedicinePsychiatryPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

Pandemic-related stressors exacerbate pre-existing mental health problems for some healthcare workers and cause others to experience problems for the first time. More resources have become available to prevent and reduce the severity of these problems. This article begins with a brief review of some of the current evidence of the problem and then outlines resources and strategies, some of which are based on the National Standard of Canada for Psychological Health and Safety in the Workplace (the Standard). The article describes a case study of the adoption of these resources to reduce stigma and improve resilience among healthcare providers. Finally, the article concludes with a call for more research which is needed to fully understand the effectiveness of these tools and resources in the context of the pandemic as well as additional tools and resources that may be needed.

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)0.007
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0040.003
Open science0.0010.008
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.036
GPT teacher head0.399
Teacher spread0.362 · 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 designNot applicable
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
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueHealthcare Management ForumSame topicWorkplace Health and Well-beingFrench-language works237,207