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Record W3155253936 · doi:10.17483/2368-6669.1235

Nursing Students in Clinical Placements Learning in Dyads: A Feasibility Study Using a Non-Randomized Pilot Trial

2021· article· en· W3155253936 on OpenAlexafffundvenueabout
Kelley Tousignant, Amanda Vandyk, Michelle Lalonde, Sophie Bigras, Sarah Roggie, Kerri-Lynn Weeks, Michelle Morley, Jean Daniel Jacob

Bibliographic record

VenueQuality Advancement in Nursing Education - Avancées en formation infirmière · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsAlgonquin CollegeUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsIntervention (counseling)Randomized controlled trialNursingProtocol (science)MedicineData collectionStrengths and weaknessesMedical educationPhysical therapyPsychologyAlternative medicine

Abstract

fetched live from OpenAlex

Purpose: To develop a protocol for a clinical education intervention using dyads and to assess the feasibility of implementing the approach with second year nursing students in their first clinical placement. The objectives were: 1) to evaluate and refine data collection procedures and outcome measures, 2) to evaluate the acceptability and sustainability of the intervention, and 3) to identify weaknesses of the intervention or threats to future implementation. Methods: A feasibility study was designed as a non-randomized pilot trial. The setting was the university site of a collaborative Bachelor of Science in Nursing program in Ottawa, Ontario. Three clinical groups consisting of 24 second-year students enrolled in both the French and English undergraduate programs comprised the sample. The intervention protocol was developed based on guiding principles reflective of the needs of our institution, as well as pedagogical priorities. Data were collected from clinical instructors and other stakeholders pre- and post-intervention through multiple means and analyzed descriptively. We followed the CONSORT extension to randomized pilot and feasibility trials to guide reporting of the study. Results: The intervention was deemed acceptable by the clinical instructors, as well as the managers and educators of the units. We received no negative feedback regarding the intervention, or the workload required to implement the intervention properly. For data collection instruments, the NSSES was completed more often and with greater ease than the VSI-NS. In all cases, the responses to the instruments were congruent with our expectations and student scores shifted in the anticipated direction. Clinical instructors were able to consistently observe patient care and reported having more time for teaching and mentorship. Conclusions: Using a dyad approach to clinical education appears to be a viable strategy, and our feasibility study supports the appropriateness of further research into the use of dyads for nursing clinical education. From a sustainability perspective, the intervention allowed for the safe implementation of larger group sizes without negatively affecting the learning environment or the integrity of the course objectives. Résumé Objectif : Élaborer un protocole d’intervention en formation clinique qui utilise des dyades et évaluer la faisabilité de mettre en place l’approche avec des étudiantes en sciences infirmières de deuxième année lors de leur premier stage clinique. Les objectifs étaient : 1) d’évaluer et de peaufiner les procédures de collecte de données et les mesures de résultats; 2) d’évaluer l’acceptabilité et la viabilité de l’intervention; et, 3) d’identifier les faiblesses de l’intervention

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)0.074
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.394

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.056
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0020.004
Open science0.0030.003
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.515
GPT teacher head0.715
Teacher spread0.200 · 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 designNon-randomized trial
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

Citations3
Published2021
Admission routes4
Has abstractyes

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