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Record W2128824042 · doi:10.5430/jnep.v5n1p107

The implementation of innovative initiatives to enhance distance learning for Australian undergraduate nursing and midwifery students

2014· article· en· W2128824042 on OpenAlexvenueno aff
Jacqueline O'Flaherty, Hayley Timms

Bibliographic record

VenueJournal of Nursing Education and Practice · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsnot available
Fundersnot available
KeywordsWorkloadDistance educationMedical educationCohortReflection (computer programming)AnxietyPsychologyNursingMedicinePedagogyComputer science

Abstract

fetched live from OpenAlex

Providing distance education students with different modes of study and interactive online learning resources has become an increasingly important part of nursing and midwifery higher education. In 2006, the number of students enrolled ‘externally’ in a compulsory foundation course (anatomy and physiology) comprised 38% of the total cohort of students (this percentage is three times the Australian HE standard for students studying externally). In response to student feedback and ongoing critical reflection a number of factors were identified that led to student dissatisfaction as well as a lower retention rate compared to the on-campus cohort. External students also reported anxiety about studying in an online environment, returning to study and study workload. In order to overcome these anxieties, provide support and assist with student retention and satisfaction, a number of innovative learning initiatives using emerging educational tools and technologies were developed and implemented. This paper discusses the results of both the early and more recent initiatives introduced into a course that was initially designed for only face-to-face students but has since been redesigned to include and support the unique needs of the external learner.

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)0.006
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.152
GPT teacher head0.605
Teacher spread0.453 · 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.

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

Citations5
Published2014
Admission routes1
Has abstractyes

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