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Record W2324127701 · doi:10.1177/0193945911408623

Perceptions of Professionalism Among Nursing Faculty and Nursing Students

2011· article· en· W2324127701 on OpenAlexaff
Noori Akhtar‐Danesh, Andrea Baumann, Camille Kolotylo, Yvonne Lawlor, Catherine Tompkins, Ruth Lee

Bibliographic record

VenueWestern Journal of Nursing Research · 2011
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsViewpointsDignityHumanismHarmPerceptionNursingPsychologyCompassionAffect (linguistics)ConsciousnessMedical educationMedicineSocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Although there is no consensus about the definition of professionalism, some generally recognized descriptors include knowledge, specialization, intellectual and individual responsibility, and well-developed group consciousness. In this study, Q-methodology was used to identify common viewpoints about professionalism held by nursing faculty and students, and four viewpoints emerged as humanists, portrayers, facilitators, and regulators. The humanists reflected the view that professional values include respect for human dignity, personal integrity, protection of patient privacy, and protection of patients from harm. The portrayers believed that professionalism is evidenced by one's image, attire, and expression. For facilitators, professionalism not only involves standards and policies but also includes personal beliefs and values. The regulators believed that professionalism is fostered by a workplace in which suitable beliefs and standards are communicated, accepted, and implemented by its staff. The differences indicate that there may be numerous contextual variables that affect individuals' perceptions of professionalism.

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)0.007
metaresearch head score (Gemma)0.018
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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.004
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.254
GPT teacher head0.531
Teacher spread0.277 · 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

Citations52
Published2011
Admission routes1
Has abstractyes

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