MétaCan
Menu
Back to cohort
Record W2017112600 · doi:10.1080/01900692.2012.655471

Supervisor Relationships, Teamwork, Role Ambiguity and Discretionary Power: Nurses in Australia and the United Kingdom

2012· article· en· W2017112600 on OpenAlexaboutno aff
Yvonne Brunetto, R Farr-Wharton, Kate Shacklock, Fiona Robson

Bibliographic record

VenueInternational Journal of Public Administration · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsTeamworkSupervisorPublic sectorAmbiguityQuarter (Canadian coin)Private sectorVariance (accounting)PsychologyPower (physics)NursingJob satisfactionSocial psychologyPublic relationsBusinessPolitical scienceMedicineAccountingGeography

Abstract

fetched live from OpenAlex

This paper reports comparative research comparing the relationship between supervisor-subordinate relationships, teamwork, role ambiguity and discretionary power for nurses working in public and private sector hospitals in Australia and the UK. The findings indicate that the four factors accounted for approximately a quarter of the variance for nurses in the UK and almost a fifth of the variance for nurses working in public sector hospitals. Moreover, the findings identify a significant difference across all variables for nurses working in public sector hospitals compared with private sector with nurses in the private sector having higher satisfaction levels and perceiving lower levels of role ambiguity. There were fewer differences for nurses working in Australian hospitals compared with UK hospitals with nurses in Australia perceiving a better supervisor-subordinate relationship and nurses in the UK perceiving greater satisfaction with teamwork.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.169
Threshold uncertainty score0.337

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
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 head0.131
GPT teacher head0.461
Teacher spread0.330 · 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

Citations26
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Public AdministrationSame topicGlobal Health Workforce IssuesFrench-language works237,207