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Record W4214542287 · doi:10.3390/healthcare10030455

Psychological Impacts of the COVID-19 Pandemic on Rural Physicians in Ontario: A Qualitative Study

2022· article· en· W4214542287 on OpenAlexaffabout
Anchaleena Mandal, Eva Purkey

Bibliographic record

VenueHealthcare · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsQueen's University
Fundersnot available
KeywordsSnowball samplingMental healthAnxietyPandemicWorkloadThematic analysisStressorBurnoutCoping (psychology)Qualitative researchPsychologyMedicineRural areaNursingPsychiatryFamily medicineClinical psychologyCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Frontline rural physicians in Canada are vulnerable to the psychological impacts of the COVID-19 pandemic considering their high pre-pandemic burnout rates as compared to their urban counterparts. This study aims to understand the psychological impacts of the COVID-19 pandemic on rural family physicians engaged in full-time primary care practice in Ontario and the stressors behind any identified challenges. Recruitment combined purposive, convenience, and snowball sampling. Twenty-five rural physicians participated in this study. Participants completed a questionnaire containing Patient Health Questionnaire-2 (depression), General Anxiety Disorder-2 (anxiety), and Perceived Stress Scale-4 (stress) screening as well as questions exploring self-reported perceptions of change in their mental health, followed by a semi-structured virtual interview. Quantitative data showed an overall increase in self-reported depression, anxiety, and stress levels. Thematic analysis revealed seven qualitative themes including the positive and negative psychological impacts on rural physicians, as well as the effects of increased workload, infection risk, limited resources, and strained personal relationships on the mental health of rural physicians. Coping techniques and experiences with physician wellness resources were also discussed. Recommendations include establishing a rapid locum supply system, ensuring rural representation at decision-making tables, and taking an organizational approach to support the mental health of rural physicians.

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)0.003
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.267
GPT teacher head0.567
Teacher spread0.300 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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