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Record W4290599849 · doi:10.3390/ijerph19159701

COVID-19 Outbreak: Understanding Moral-Distress Experiences Faced by Healthcare Workers in British Columbia, Canada

2022· article· en· W4290599849 on OpenAlexaffabout
Esther Alonso‐Prieto, Holly Longstaff, Agnes Black, Alice Virani

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsSimon Fraser UniversityProvidence Health CareProvincial Health Services AuthorityB.C. Women's Hospital & Health CentreUniversity of British Columbia
Fundersnot available
KeywordsPandemicThematic analysisEmpathyCoping (psychology)Health careFeelingDistressQualitative researchAnxietyPsychologyLearned helplessnessMoral injuryPublic healthNursingMedicineCoronavirus disease 2019 (COVID-19)PsychiatryClinical psychologySocial psychologyPolitical scienceSociologyDisease

Abstract

fetched live from OpenAlex

Pandemic-management plans shift the care model from patient-centred to public-centred and increase the risk of healthcare workers (HCWs) experiencing moral distress (MD). This study aimed to understand HCWs' MD experiences during the COVID-19 pandemic and to identify HCWs' preferred coping strategies. Based on a qualitative research methodology, three surveys were distributed at different stages of the pandemic response in British Columbia (BC), Canada. The thematic analysis of the data revealed common MD themes: concerns about ability to serve patients and about the risks intrinsic to the pandemic. Additionally, it revealed that COVID-19 fatigue and collateral impact of COVID-19 were important ethical challenges faced by the HCWs who completed the surveys. These experiences caused stress, anxiety, increased/decreased empathy, sleep disturbances, and feelings of helplessness. Respondents identified self-care and support provided by colleagues, family members, or friends as their main MD coping mechanisms. To a lesser extent, they also used formal sources of support provided by their employer and identified additional strategies they would like their employers to implement (e.g., improved access to mental health and wellness resources). These results may help inform pandemic policies for the future.

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)0.013
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.790
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.244
GPT teacher head0.505
Teacher spread0.261 · 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

Citations19
Published2022
Admission routes2
Has abstractyes

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