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Record W2895569946 · doi:10.5737/23688076284288293

Building and sustaining a postgraduate student network: The experience of oncology nurses in Canada

2018· article· en· W2895569946 on OpenAlexaffvenueabout
Jacqueline Galica, Karine Bilodeau, Fay J. Strohschein, Tracy Powell, Leah K. Lambert, Tracy Truant

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsMount Royal UniversityUniversité de MontréalUniversity of British ColumbiaMcGill UniversityQueen's University
Fundersnot available
KeywordsProfessional developmentMedical educationSpace (punctuation)PsychologyOncologyMedicine

Abstract

fetched live from OpenAlex

Networking with individuals on a similar journey through graduate studies has been identified as an important influence in the experience of doctoral and postdoctoral students. Through the Board of Directors of the Canadian Association of Nurses in Oncology/Association canadienne des infirmières en oncologie (CANO/ACIO), student members were encouraged to establish a Doctoral Student Network (DSN) that would enable a connection through education and common interest in oncology and cancer care. Since its inception, the DSN has been dynamic in how it has addressed the evolving needs of members and in providing development opportunities to group members. To uncover and describe key aspects of its evolution, a document analysis was undertaken to reveal themes pertaining to capacity development and leadership voice as paths to leadership for DSN members. The results of this study suggest that the DSN provides a supportive environment for postgraduate nurses across Canada to connect with others in their peer group to foster engagement in educational, professional, and scholarly activities, as well as highlighting opportunities for other professional organizations interested in establishing a support network for graduate student members.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.545
Teacher spread0.412 · 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.

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

Citations5
Published2018
Admission routes3
Has abstractyes

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