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Record W3049590706 · doi:10.24384/2wjj-py19

Clients’ and Coaches’ Perspectives of a Life Coaching Intervention for Parents with Overweight/Obesity

2020· article· en· W3049590706 on OpenAlexaff
Shazya Karmali, Danielle S. Battram, Shauna M. Burke, Anita G Cramp, Tara Mantler, Don Morrow, Victor Ng, Erin S. Pearson, Robert J. Petrella, Patricia Tucker, Jennifer D. Irwin

Bibliographic record

VenueRadar (Oxford Brookes University) · 2020
Typearticle
Languageen
FieldPsychology
TopicCoaching Methods and Impact
Canadian institutionsLakehead UniversityUniversity of TorontoMiddlesex London Health UnitWestern University
Fundersnot available
KeywordsCoachingOverweightIntervention (counseling)ObesityPsychologyMedicineDevelopmental psychologyPsychotherapistPsychiatryEndocrinology

Abstract

fetched live from OpenAlex

This qualitative study explored the coaching-related experiences of clients (parents who were overweight/obese) and coaches who participated in a 3-month obesity intervention. Semi-structured interviews were conducted at multiple time points and were audio-recorded and analysed by question and via inductive content analysis. Clients reported increased accountability, goal setting skills, awareness, and external support in relation to health behaviours. Coaches shared tools they utilised, insights from working with this population, and advice for future coaches. This research informs the client-coach relationship; insights from both parties will allow researchers to create effective programming for this population.

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)0.005
metaresearch head score (Gemma)0.010
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.277
Teacher spread0.244 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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