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Record W4286213332 · doi:10.1101/2022.07.18.22277694

Determinants of healthcare employees’ preference to continue teleworking after the COVID-19 pandemic: a cross-sectional study using hierarchical regression

2022· preprint· en· W4286213332 on OpenAlexafffund
Andrea M. Jones, Jonathan Fan, Leah Thomas-Olson, Wei Zhang, Chris McLeod

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsSt. Paul's HospitalInstitute for Work & HealthCentre for Advancing Health OutcomesFraser HealthUniversity of British Columbia
FundersUniversity of British ColumbiaMichael Smith Health Research BCWorkSafeBC
KeywordsPreferencePandemicMultinomial logistic regressionWorkforceCross-sectional studyProductivityCoronavirus disease 2019 (COVID-19)Health careLogistic regressionMultilevel modelBusinessMedicineEconomicsComputer scienceEconomic growth

Abstract

fetched live from OpenAlex

Abstract Employees’ post-pandemic telework preference is an important consideration for navigating post-pandemic work arrangements and can inform organizational planning and workforce management. A cross-sectional survey of employees (n=400, participation rate =36.4%) of a regional health authority who teleworked during the COVID-19 pandemic was conducted. The most common post-pandemic telework preference was all the time (52%) followed by over half but not all the time (32%) and less than half the time or not at all (16%). Using hierarchical multinomial logistic regression models and less than half the time or not at all as the reference outcome, being a provider of direct patient care and productivity while teleworking were strong determinants of post-pandemic telework preference while two or more weekly teleconference hours, work-life balance and having one or more people over five years of age in the home while teleworking were moderate determinants.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.002
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 head0.211
GPT teacher head0.447
Teacher spread0.236 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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