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Record W2093082365 · doi:10.4236/ojpm.2014.46058

The Impact of a Community Pharmacy-Based Coaching Program on Patient Confidence and Lifestyle

2014· article· en· W2093082365 on OpenAlexaff
Feng Chang, Nishi Gupta, Laura Smith, Dan Stringer

Bibliographic record

VenueOpen Journal of Preventive Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCoachingPharmacyMedicineHealth coachingFamily medicineConfidence intervalRural areaGerontologyNursingDiseasePsychologyInternal medicine

Abstract

fetched live from OpenAlex

Objective: Like their urban counterparts, rural populations are experiencing increased health risks due to chronic disease. However, disease management is more problematic due to isolation, increased difficulty in getting to medical appointments, and reduced numbers of medical personnel. We undertook a pilot study to investigate the feasibility and utility of health coaching for rural residents with type 2 diabetes mellitus (DM2) from a local pharmacy. Methods: Using the pharmacy database to identify qualified individuals, a nursing student recruited four individuals aged 40 - 79, with a history of DM2 of 3 - 15 years, to participate in the pilot project. Individual in-person interviews were conducted to identify specific goals to effectively self-manage their condition and to rate their confidence in their ability to fulfill these goals. Three monthly sessions were held to review and update goals, and to record blood pressure, waist circumference and weight measurements. Results: At the end of the study, all four achieved success in reaching and maintaining their personal dietary and physical activity goals. Significantly, all participants expressed increased confidence in their ability to self-manage their diabetes after health coaching. Conclusion: The provision of health coaching services from local pharmacies has potential to support rural clients in chronic disease management in medically under-serviced rural areas.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.764
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
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 integrity0.0000.001
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 head0.152
GPT teacher head0.503
Teacher spread0.351 · 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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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