MétaCan
Menu
Back to cohort

Correlates of Nursing Staff Survivor Responses to Hospital Restructuring and Downsizing

2005· article· en· W2316753127 on OpenAlexaff
Ronald J. Burke

Bibliographic record

VenueThe Health Care Manager · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Downsizing and Restructuring
Canadian institutionsYork University
Fundersnot available
KeywordsRestructuringPerceptionNursingPsychologyNursing staffWork (physics)Clinical psychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

This study examines correlates of 4 archetypal survivor responses to organizational restructuring and downsizing proposed by Mishra and Spreitzer: hopeful, obliging, cynical, and fearful. Data were collected from 744 long-term nursing staff survivors of hospital restructuring and downsizing using questionnaires. Three types of correlates were considered: work outcomes, indicators of psychologic well-being, and perceptions of hospital functioning. Greater endorsement of cynical and fearful restructuring responses was associated with more negative work outcomes and lower psychologic well-being. Greater endorsement of both cynical and fearful responses was also found to be associated with more negative perceptions of hospital functioning and effectiveness.

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)0.001
metaresearch head score (Gemma)0.011
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.254
Teacher spread0.244 · 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

Citations11
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Health Care ManagerSame topicOrganizational Downsizing and RestructuringFrench-language works237,207