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Record W2173110350 · doi:10.5539/ies.v8n12p13

Competency Management and Learning Organization in a New Clinical Fieldwork Course

2015· article· en· W2173110350 on OpenAlexvenueno aff
Supawadee Putthinoi, Suchitporn Lersilp, Nopasit Chakpitak

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
FundersGraduate School, Chiang Mai UniversityChiang Mai University
KeywordsLearning organizationMaturity (psychological)PsychologyAdaptation (eye)Class (philosophy)Process (computing)Medical educationService-learningKnowledge managementService (business)PedagogyBusinessMedicineComputer scienceMarketing

Abstract

fetched live from OpenAlex

<p class="apa">As Thailand transitions into an ageing society, greater demands will be placed on healthcare systems. The concept of competency management and learning organization can be beneficial in continually expanding organizational capacity in order to create response. This study aimed to develop a new clinical fieldwork course in the community by utilizing competency-based development and assessment within the learning organization concept. Learning outcomes such as specification of programs, courses and field experience were designed under the competency standards of the Thai Qualifications Framework (TQF) for Higher Education and the World Federation of Occupational Therapists (WFOT).The household environmental risk management course was developed to support elderly people living in the community. It aligns with the new requirements for clinical fieldwork, which are guided by research and development (teaching, research and service) concepts. Course must provide a proactive approach with the potential to extend the capacity for continual learning, innovation and adaptation. Thus, academics can utilize research findings to improve the process, continually developing the quality and maturity of the organization over time.</p>

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)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.756

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.157
GPT teacher head0.536
Teacher spread0.378 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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