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Record W2814468280 · doi:10.1177/2374373518786505

Welcoming Feedback: Using Family Experience to Design a Pediatric Weight Management Program

2018· article· en· W2814468280 on OpenAlexaff
Jennifer Green, Alexandra Wills, Elizabeth Mansfield, Deepy Sur, Ian Zenlea

Bibliographic record

VenueJournal of Patient Experience · 2018
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of TorontoTrillium Health Centre
Fundersnot available
KeywordsPDCAExperiential learningQuality managementMedical educationProcess managementPsychologyComputer scienceMedicineOperations managementEngineeringPedagogyManagement system

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe an approach using principles of experience-based codesign (EBCD) and quality improvement (QI) to integrate family experience into the development of a pediatric weight management program. METHODS: Clinic development occurred in 3 plan, do, study, act (PDSA) cycles that were driven by family experience data. During these cycles, families were engaged in feedback sessions that informed program development. Staff reflected on feedback and designed and tested changes that would improve service delivery. RESULTS: The first PDSA cycle resulted in the fundamental program parameters and a formalized patient engagement strategy. The second cycle focused on pilot programming, and feedback was used to develop the structured group program. During the third cycle, feedback sessions were embedded into the structured group programs. Program changes included focusing on health rather than weight-based outcomes, adjusting the timing of program offerings, increasing experiential learning opportunities, and providing more opportunities for peer support. CONCLUSIONS: Both EBCD and QI methodologies informed the process of family engagement and program development. This pragmatic approach might be useful for the development of other family-centered pediatric programs.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.129
GPT teacher head0.478
Teacher spread0.349 · 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.

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

Citations5
Published2018
Admission routes1
Has abstractyes

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