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Record W1675313651 · doi:10.5539/jel.v4n3p146

Sensitization Sessions as the Foundation for Training Transformation Activities

2015· article· en· W1675313651 on OpenAlexafffundvenueabout
Sacha Stoloff, Maude Boulanger, Virginie Roy, Marie-Claude Rivard

Bibliographic record

VenueJournal of Education and Learning · 2015
Typearticle
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersUniversité du Québec à Trois-Rivières
KeywordsTrainerPublic relationsAction (physics)PsychologySession (web analytics)Focus groupMedical educationPolitical scienceBusinessMedicineMarketingAdvertisingComputer science

Abstract

fetched live from OpenAlex

The worldwide rise in obesity makes this the first non-infectious epidemic in human history. The rapid increase is, in fact, influenced more by environment than biology. In an effort to halt the trend, Quebec has launched a major awareness-raising campaign that focuses on healthy environments and targets stakeholders in schools, municipalities, communities and the health sector. The purpose of the present study, then, is to determine how this campaign can promote action towards environments conducive to healthy lifestyles. The theoretical framework is based on planned change. The objectives are to 1) evaluate the quality of awareness-raising methods offered by trainers, and 2) place the impacts of the sessions into perspective. A qualitative approach was prioritized, consisting of two focus groups conducted with 17 trainers. From the standpoint of a healthy environment, sensitization sessions expanded networking, provided a common frame of reference and drove coherent actions for stakeholders involved. As an agent of change, the trainer played a key role in implementing the sessions. The conditions offered encouraged the transition from awareness to information, thereby generating significant results in terms of action. A sensitization session is thus a prerequisite for training transformation activities aimed in innovation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.063
GPT teacher head0.411
Teacher spread0.348 · 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 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 routes4
Has abstractyes

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