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Record W2750691521 · doi:10.36510/learnland.v10i2.813

smartEducation: Developing Stress Management and Resiliency Techniques

2017· article· en· W2750691521 on OpenAlex
Karen Ragoonaden

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueLEARNing Landscapes · 2017
Typearticle
Languageen
FieldPsychology
TopicCOVID-19 and Mental Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMindfulnessVariety (cybernetics)Stress managementMedical educationPsychological resiliencePsychologyCurriculumProfessional developmentResilience (materials science)Face (sociological concept)PedagogyApplied psychologyMedicineClinical psychologyPsychotherapistSociologyComputer science

Abstract

fetched live from OpenAlex

smartEducation (Stress Management and Resiliency Techniques) is a mindfulness-based professional learning initiative positioned in a Faculty of Education of a Western Canadian university. Following similar evidence-based initiatives of mindfulness in education, the smartEducation curriculum comprises nine sessions offered in a variety of face-to-face, intensive, and blended formats. This renewal program supports the development of self-care techniques to cultivate personal and professional resilience through a greater understanding and control of breath, movement, and the physiology of emotions. The 20-hour program consists of eight two-hour sessions and a four-hour silent retreat. This article provides an overview of the research supporting mindfulness in education and presents the results of a pilot study conducted with preservice teachers enrolled in the smartEducation course.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.039
GPT teacher head0.394
Teacher spread0.355 · 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