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Record W2765651656 · doi:10.24926/iip.v8i4.928

Promoting Peer Debate in Pursuit of Moral Reasoning Competencies Development: Spotlight on Educational Intervention Design

2017· article· en· W2765651656 on OpenAlexaff
Cicely Roche, Marek Radomski, Tamasine Grimes, Steve Thoma

Bibliographic record

VenueINNOVATIONS in pharmacy · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPsychological interventionIntervention (counseling)DilemmaPsychologyAction (physics)Medical educationEngineering ethicsPedagogyMedicineEngineeringEpistemology

Abstract

fetched live from OpenAlex

Research indicates that appropriately designed educational interventions may impact positively on moral reasoning competencies development (MRCD) as measured by a psychometric measure known as the Defining Issues Test (DIT). However, findings include that educational interventions intended to impact on MRCD do not consistently promote measurable pre-post development. This paper reviews the theoretical background to the use of educational interventions to impact on MRCD, and spotlights how underpinning Neo-Kohlbergian theory might inform the design of an intervention in order to optimise impact on MRCD. Findings indicate that peer debate - regarding ethical concepts in profession-specific dilemma scenarios, what action(s) might be taken and how ‘less than ideal’ action options might be justified - is essential. Five examples of an adapted format of ‘Neo-Kohlbergian’ profession-specific ‘intermediate concept measures’ (ICMs) are included and were integrated into a 16 week blended learning educational intervention in a manner that promoted repeated exposure to peer debate regarding dilemmas, and the educational intervention design was trialled in a study with 27 volunteer community pharmacists in Ireland. An overview of the design, development and delivery of the intervention is provided. The paper concludes with recommendations for further development of the ‘idea’. Conflict of Interest: None Type: Idea Paper

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)0.008
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.388
GPT teacher head0.491
Teacher spread0.103 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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