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Record W1925299430 · doi:10.3109/01612840.2014.968694

Towards Effective Management in Psychiatric-Mental Health Nursing: The Dangers and Consequences of Micromanagement

2015· article· en· W1925299430 on OpenAlex
Michelle Cleary, Catherine Hungerford, Violeta López, John R. Cutcliffe

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIssues in Mental Health Nursing · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsWycliffe College
Fundersnot available
KeywordsMental healthProductivityCreativityManagement stylesPsychologyNursingMedicinePsychiatrySocial psychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Micromanagement refers to a management style that involves managers exercising control over team members, teams, and also organizations, particularly in relation to the minutiae or minor details of day-to-day operations. While there is no single reason why some managers may choose to micromanage, many micromanagers exhibit similar behavioral traits, a consequence of perfectionism and/or underlying insecurities. In the culture of high performance that characterizes many contemporary mental health contexts, micromanagement also provides one way by which teams can be driven to achieve targets. However, over time, micromanagement leads to reductions in staff morale, creativity, and productivity; and increases in staff turnover. This paper provides an overview of micromanagement, including points of consideration for managers interested in reflecting on their management styles, and strategies for mental health nurses who find themselves working for a micromanager.

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.336
Teacher spread0.308 · 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