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Record W4230236293 · doi:10.1037/e676132011-007

Hospital restructuring and downsizing: Effects on nursing staff well-being and perceived hospital functioning

2011· dataset· en· W4230236293 on OpenAlexafffund
Ronald J. Burke, Eddy S. Ng, Jacob Wolpin

Bibliographic record

VenuePsycEXTRA Dataset · 2011
Typedataset
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsYork UniversityDalhousie University
FundersDalhousie UniversityYork University
KeywordsRestructuringNursing staffNursingPsychologyMedicineBusinessFinance

Abstract

fetched live from OpenAlex

The health care system, and hospitals, underwent considerable restructuring and downsizing in the early to mid-1900s in several countries as governments cut costs to reduce their budget deficits.Studies of the effects of these efforts on nursing staff and hospital functioning in various countries generally reported negative impacts.Health care restructuring and hospital downsizing was again being implemented in North America in 2009/2010 as governments struggled to once again reduce deficits at a time of worldwide economic recession.This study examines the relationship of downsizing and restructuring efforts with work and well-being outcomes.Data were collected from over 289 nursing staff working in California hospitals in 2009/2010.This research considers the relationship of number of hospital restructuring initiatives reported by nursing staff with indicators of their work satisfaction and psychological well-being and their perceptions of the impact of these initiatives on aspects of hospital performance.Nurses reported a relatively large number of restructuring and downsizing initiatives during the preceding year.Consistent with findings reported over 15 years ago, nursing staff reporting a greater number of restructuring and downsizing initiatives indicated less favorable work and well-being outcomes and more negative effects on hospital functioning.Some suggestions for more successful approaches to cost reductions are offered.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.056
GPT teacher head0.362
Teacher spread0.306 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2011
Admission routes2
Has abstractyes

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