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Record W2197528354 · doi:10.17483/2368-6669.1062

An Exploration of the Pre-tenure and Tenure Process Experiences of Canadian Nursing Faculty

2016· article· en· W2197528354 on OpenAlexaffvenueabout
M. Singh, Linda J. Patrick, Beryl Pilkington

Bibliographic record

VenueQuality Advancement in Nursing Education - Avancées en formation infirmière · 2016
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsUniversity of WindsorToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsMentorshipEmpowermentOrganizational cultureQualitative researchWork (physics)Process (computing)PsychologyWork environmentNursingPublic relationsMedical educationBusinessSociologyPolitical scienceMedicineSocial psychologyJob satisfaction

Abstract

fetched live from OpenAlex

Background: The number of PhD-prepared nurses seeking employment in academia in Canada is not keeping up with the rate of retirements and the demands for new hires in the same settings. The current number of vacancies is expected to grow over the coming decade as an aging professoriate prepares to leave full-time employment. Retention of newly hired faculty will become a critical issue for administrators in an increasingly competitive environment. Purpose: The purpose of this mixed-methods study was to explore how organizational culture, mentorship, and the perceived level of psychological and structural empowerment are associated with one’s work environment among pre-tenure and newly tenured nursing faculty in Canada. Methods: This article reports the qualitative findings from the in-depth, semi-structured interviews conducted with 10 faculty volunteers after they completed an online survey. Results: Respondents overwhelmingly expressed a desire for a collegial and supportive working environment with clearly articulated policies and a transparent process for achieving tenure in academia. A healthy work environment was clearly identified as critical to the tenure process. Mentorship was identified as critical to creating a productive research culture.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.076
GPT teacher head0.446
Teacher spread0.371 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations8
Published2016
Admission routes3
Has abstractyes

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