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Record W2342033724 · doi:10.17483/2368-6669.1060

A Capstone Project: A way to Integrate Knowledge and Empower Students to Become Change Agents in the Practice Setting.

2016· article· en· W2342033724 on OpenAlexaffvenueabout
Louela Manankil‐Rankin, Ola Lunyk Child, Ruth Chen, Lynn Martin, Lynda Bentley Poole

Bibliographic record

VenueQuality Advancement in Nursing Education - Avancées en formation infirmière · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsMcMaster UniversityNipissing University
Fundersnot available
KeywordsCapstoneKnowledge managementEngineering ethicsComputer scienceProcess managementEngineering managementMedical educationBusinessEngineeringMedicine

Abstract

fetched live from OpenAlex

Nursing education today calls for creative and innovative ways in preparing students for the future. The growing complexity in the practice setting demands that nursing students gain exposure to skill sets that empower them to be change agents. A capstone project in the final year of an Undergraduate Nursing Program enabled students to integrate their knowledge from all areas of their curriculum to address quality improvement (QI) issues. While the students’ projects were not QI projects, they functioned as precursors or ideas for future QI initiatives. The capstone projects were graded using a designed marking rubric that addressed the Undergraduate Degree Level Expectations and the entry to practice competencies for the Registered Nurse in Ontario. The students presented their work at a capstone conference that invited personnel from both academia and clinical practice organizations. The capstone project became a way to acknowledge ideas and accomplishments of future nursing graduates.

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)0.017
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 score0.035
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0050.004
Open science0.0020.014
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0350.014

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 head0.078
GPT teacher head0.500
Teacher spread0.422 · 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

Citations2
Published2016
Admission routes3
Has abstractyes

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