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Record W2376779969 · doi:10.5430/jha.v5n4p76

Working on the frontlines in U. S. hospitals: Scheduling challenges and turnover intent among housekeepers and dietary service workers

2016· article· en· W2376779969 on OpenAlexvenueno aff
Jennifer E. Swanberg, Helen M. Nichols, Maureen Perry‐Jenkins

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
Fundersnot available
KeywordsHousekeepingTurnoverWork scheduleMedicineScheduleHealth careNursingOperations managementScheduling (production processes)Management

Abstract

fetched live from OpenAlex

The changing nature of U.S. health care has created challenges for hospitals, including managing high rates of turnover among frontline support workers. Few studies have examined the effect of work schedules on turnover among frontline hospital support workers. This cross-sectional study (N = 270) examined scheduling challenges prevalence among hospital housekeeping and dietary workers and their relationship to turnover intent. Both worker groups experienced schedule unpredictability but dietary workers reported greater schedule instability than housekeeping workers. Schedule rigidity was reported by all workers, although housekeepers had more difficulty in changing work schedules. All three scheduling challenges were significant predictors of intent to leave.

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 score0.056
Threshold uncertainty score0.380

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 integrity0.0000.001
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 head0.038
GPT teacher head0.327
Teacher spread0.289 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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