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Record W2087022534 · doi:10.5430/jnep.v5n6p1

What went wrong? A critical reflection on educator midwives’ inability to transfer education knowledge

2015· article· en· W2087022534 on OpenAlexvenueno aff
Yvonne Botma, Champion N. Nyoni

Bibliographic record

VenueJournal of Nursing Education and Practice · 2015
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumReflection (computer programming)ConstructiveAction researchReflective practicePedagogyAction learningPsychologyAction (physics)MedicineNursingMedical educationTeaching methodProcess (computing)Cooperative learningComputer science

Abstract

fetched live from OpenAlex

As part of a competency-based curriculum development exercise, educator midwives were required to apply the design principles of constructivism, constructive alignment, scaffolding and authenticity in the development of teaching and learning material for a newly approved curriculum. Through action research cycles, the facilitators and educator midwives reflected on possible reasons why they struggled to apply the mentioned principles in developing learning activities for students. The unit of analysis comprised the reflections of facilitators and 12 educator midwives. Ten of the 12 educator midwives were older than 40 years and all had qualifications and experience in midwifery and education. The action–reflection cycles contributed to improvement in the quality of the learning activities but application of the scaffolding principle remained a challenge. Failure of the educator midwives to transfer their learning raised concerns about their ability to facilitate deep learning. Considering the age of the group and the ingrained rote memorisation characteristic of education methods during their training made curriculum drift a real threat. Educator midwives struggled to integrate education knowledge into educational practice. Nursing schools that are in the process of changing their teaching paradigm may find this article useful to identify possible challenges and suggested solutions.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.140
GPT teacher head0.507
Teacher spread0.368 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations16
Published2015
Admission routes1
Has abstractyes

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