MétaCan
Menu
Back to cohort

Enhancing Self-Reflective Practice and Conscious Service in the Helping Professions

2015· book-chapter· en· W2486166361 on OpenAlexaff
Elizabeth Bishop

Bibliographic record

VenueAdvances in higher education and professional development book series · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsConfederation College
Fundersnot available
KeywordsReflective practiceCurriculumContext (archaeology)PsychologyProfessional developmentMedical educationPedagogyValue (mathematics)Mental healthEngineering ethicsMedicineEngineeringPsychotherapistComputer science

Abstract

fetched live from OpenAlex

This chapter highlights the results and the implications of research that was conducted with a group of mental health practitioners involved in an eight-week educational program designed to help establish and enhance self-reflective practice. The study elicited a number of emerging themes related to the benefits of self-reflective practice in the helping professions. The research project took place within the context of a professional setting; however, emphasis and value was placed upon the informal learning experiences of the participants. While there were a number of findings and recommendations obtained through this research project, the main focus for this chapter centers on the role of informal learning as an element of the research project design and ultimately as an integral component of self-reflective practice. The highlighted results include two elements of the findings related to curriculum design and effective facilitative strategies that might be helpful to adult educators involved in post-secondary education as well as continuing education and professional development activities with an emphasis on maximizing the benefits of informal learning.

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)0.007
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.415
Teacher spread0.370 · 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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in higher education and professional development book seriesSame topicReflective Practices in EducationFrench-language works237,207