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Record W2056313627 · doi:10.5430/jha.v1n2p17

Evaluating the implementation of health coaching in a rural setting

2012· article· en· W2056313627 on OpenAlexvenueno aff
Kaye Ervin, Vivienne Jeffery, Alison Koschel

Bibliographic record

VenueJournal of Hospital Administration · 2012
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsnot available
Fundersnot available
KeywordsCoachingMedicineIntervention (counseling)NursingTraining (meteorology)Health careMedical educationHealth coachingPsychology

Abstract

fetched live from OpenAlex

Objective: The aim of this project was to explore the barriers and enablers to implementation of staff training in Health Coaching, a model of care employed in primary care to facilitate client self management of chronic disease.Methods: Forty six staff from five rural community health settings were recruited to undertake training in Health Coaching. A simple post training quantitative evaluation was conducted by surveying staff five months post training. Results: There was a 68% response rate to the surveys. Only 50% of staff trained in Health Coaching reported implementing it into practice. Enabling factors to implementing the training were reported as peer and organisational support.Conclusion: Effective models of self management in chronic disease should not be aimed at staff training alone. This study suggests that implementation of new models of care requires a significant change in clinician practice which is not readily embraced by staff. Key words: chronic disease, early intervention, self management, staff training, training implementation.

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)0.011
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.429
Teacher spread0.389 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

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