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Record W2908007839 · doi:10.5430/jnep.v9n5p6

Successful vs. unsuccessful small group reflection: A narrative inquiry

2018· article· en· W2908007839 on OpenAlexaffvenue
Ping Zou, Arthy Visayanathan, Christine Whyte, Alla Pak, Angela Cooper Brathwaite, Qiongli Zhu, Rick Vanderlee

Bibliographic record

VenueJournal of Nursing Education and Practice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsOntario Tech UniversityNipissing University
Fundersnot available
KeywordsReflection (computer programming)NarrativePracticumPsychologyPedagogyNarrative inquiryMedical educationMathematics educationMedicineComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

There is a lack of research examining the factors which promote or hinder successful small group clinical practicum reflection seminars. The aim of this study was to explore key elements of successful small group reflection. Narrative inquiry was used as methodology. Three students consented to voluntarily participate in this study by learning on their experiences – both successful and unsuccessful – during their clinical reflection seminars. A 3-circle model was presented as a collective narrative. The Support Circle represents a safe and supportive environment where the reflection seminar is held. The Owner Circle represents the students’ ownership in a reflection seminar. The Service Circle represents the educators’ professional teaching services. To conclude, elements of a successful small group reflection included a safe leaning environment, a student-centered approach, and professional educator support. Within a safe learning environment, a successful small group reflection seminar should be owned by students and facilitated by a professional educator.

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)0.040
metaresearch head score (Gemma)0.112
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.112
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0040.006
Scholarly communication0.0080.009
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.136
GPT teacher head0.511
Teacher spread0.375 · 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 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

Citations2
Published2018
Admission routes2
Has abstractyes

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