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Record W7116086955 · doi:10.65638/2978-5634.2025.01.09

Teaching Research Proposal Writing in Nursing: A Step-by-Step Pedagogical Framework

2025· article· W7116086955 on OpenAlexaff

Bibliographic record

VenueJournal of Teaching Innovation and Reform · 2025
Typearticle
Language
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsExperiential learningProcess (computing)Research proposalLifelong learningCore competencyNurse educationResearch ethicsProfessional development

Abstract

fetched live from OpenAlex

Research proposal writing is a core competency in nursing education, equipping students to design, evaluate, and apply evidence, based inquiry in professional practice. This article reframes the process of developing a nursing research proposal as a pedagogical framework for advancing teaching innovation in nursing research education. Using a structured, step, by, step model, it demonstrates how proposal development can be embedded into research methods courses to strengthen students’ analytical reasoning, ethical awareness, and research literacy. The paper outlines strategies such as scaffolded assignments, peer collaboration, and simulation, based ethics training that transform proposal writing into an active and experiential learning process. By positioning proposal development as both a teaching tool and a curricular reform strategy, this approach enhances students’ ability to connect theory with application, fostering lifelong engagement with evidence, based practice and scholarly inquiry.

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)0.138
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.138
Threshold uncertainty score0.728

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1380.088
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.002
Science and technology studies0.0070.025
Scholarly communication0.0130.012
Open science0.0080.016
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0030.003

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 head0.246
GPT teacher head0.592
Teacher spread0.346 · 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 designNot applicable
Domainnot available
GenreMethods

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 routes1
Has abstractyes

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