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Record W4377982445 · doi:10.1007/s10834-023-09898-9

A Workplace Environmental Scan of Employed Carers During COVID-19

2023· article· en· W4377982445 on OpenAlexafffundabout
Regina Ding, Jenny Ploeg, Allison Williams

Bibliographic record

VenueJournal of Family and Economic Issues · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsMcMaster UniversityCarleton University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsPresenteeismPandemicWork (physics)Coronavirus disease 2019 (COVID-19)NursingSupervisorHealth carePsychologyWork scheduleBusinessMedicineAbsenteeismSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

The carer-employee experience has undergone multiple shifts during the COVID-19 pandemic. This study seeks to understand how changes in the workplace as a result of the pandemic have impacted employed carers with their ability to perform both care obligations and paid work responsibilities. Using an online workplace-wide survey at a large Canadian firm, we conducted an environmental scan of: the current state of workplace supports and accommodations, supervisor attitudes, and carer-employee burden and health. Our findings demonstrate that while employees are generally in good health, care burden and time spent caregiving has been higher during COVID-19. Notably, employee presenteeism is higher during the pandemic than it was previously, with carer-employees experiencing significantly reduced levels of co-worker support. The most common workplace adaptation to COVID-19, work-from-home, was preferred by all employees as it allowed greater schedule control. However, this comes at the cost of reduced communications and sense of workplace culture, especially for carer-employees. We identified several actionable changes within the workplace, including: greater visibility of existing carer resources, and standardized training of managers on carer issues.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.315
Teacher spread0.278 · 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.

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

Citations2
Published2023
Admission routes3
Has abstractyes

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