MétaCan
Menu
Back to cohort
Record W4380632608 · doi:10.5864/d2023-002

Understanding the multidimensional aspects of quality of work-life of Environmental Public Health Professionals in Canada

2023· article· en· W4380632608 on OpenAlexaffvenueabout
Fatih Şekercioğlu, Yamin Tauseef Jahangir, Anne-Maria Korpikoski, Ian Young, Richard Meldrum

Bibliographic record

VenueEnvironmental Health Review · 2023
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWorkloadBurnoutPublic healthWorkforceWork (physics)Quality of life (healthcare)Mental healthAnxietyEnvironmental healthPsychologyPandemicNursingGerontologyMedicineCoronavirus disease 2019 (COVID-19)Political sciencePsychiatryEngineeringManagementClinical psychology

Abstract

fetched live from OpenAlex

Environmental Public Health Professionals (EPHPs) are an essential frontline workforce that aims to keep the Canadian population healthy and safe. Our study explores the multidimensional aspects of work-life related Quality of Work (QoW) among EPHPs in Canada. A mixed-method cross-sectional online survey was used. The data collection was completed in September–October 2022 with 96 participants. The study included EPHPs such as Public Health Inspectors and Environmental Health Officers in Canada who are currently working in Canada and during the COVID-19 pandemic and managers/supervisors of EPHPs. Our Study results reveal that there have been significant challenges to ensuring QoW in work-life settings for EPHPs, ranging from negative attitudes from the employer, challenges with varying guidelines due to geographic locations of health authorities, and increased levels of workload leading to stress, anxiety, depression and burnout – all of which has affected the work-life balance and mental health and well-being of EPHPs.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.338

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.006
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.202
GPT teacher head0.424
Teacher spread0.222 · 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 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

Citations0
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueEnvironmental Health ReviewSame topicWorkplace Health and Well-beingFrench-language works237,207