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Record W4409366067 · doi:10.1515/ijnes-2022-0029

The Doctoral Seminar in nursing: an exploration of the literature and trends found in Canadian syllabi

2025· review· en· W4409366067 on OpenAlexaffabout
Cheryl van Daalen‐Smith, Ramesh Venkatesaperumal, Samantha Johnson, S. Sonja Cairns, Bev Beattie, Billie Hilborn

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2025
Typereview
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsNipissing UniversitySeneca PolytechnicYork University
Fundersnot available
KeywordsSyllabusScholarshipMedical educationNursing researchPedagogyPsychologyNursingSociologyMedicinePolitical science

Abstract

fetched live from OpenAlex

OBJECTIVES: A success-intended Doctoral Seminar, intentionally structured and facilitated to support students to thrive in their achievement of degree-level milestones, is assumedly the raison d'etre of the commonly-scheduled pass/fail Doctoral Seminar course in nursing doctoral programs. In this article, we review the literature regarding the Doctoral Seminar and the trends found in available Canadian Doctoral Seminar Syllabi. METHODS: A review of the literature guided by Finfgeld-Connett and Johnson's process; and, a document analysis of collected doctoral syllabi. RESULTS: Our review of Canadian Doctoral Seminar syllabi illuminated consistent attention to enabling the growth and development of doctoral skills, and a propensity towards higher-level thinking with scaffolded efforts to advance the production of new and original scholarship. Pedagogically, facilitation was the model and engagement with students was the strategy, giving way to student voice and a co-created experience. CONCLUSIONS: With global growth in doctoral nursing education, an international exploration of the best use of the doctoral seminar is therefore an imperative.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.384
GPT teacher head0.637
Teacher spread0.254 · 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 designOther design
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes2
Has abstractyes

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