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Record W4200566218 · doi:10.1177/22925503211042870

Facilitating Behavior Change in Plastic Surgery Patients Who Inject Drugs Through Motivational Interviewing

2021· article· en· W4200566218 on OpenAlexaff
Oluwatobi R. Olaiya, Awwal Alagabi, Sonia Igboanugo, Morgan L. Glass, Mark McRae, Alfred Amaladoss

Bibliographic record

VenuePlastic Surgery · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of TorontoYork UniversityMcMaster University
Fundersnot available
KeywordsMotivational interviewingInterviewBehavior changeBehaviour changePsychologyHealth careClinical PracticeMedicineApplied psychologyPsychotherapistNursingSocial psychologyIntervention (counseling)

Abstract

fetched live from OpenAlex

Background: Plastic surgeons treat a large volume of patients with upper limb morbidity resulting from intravenous drug use. The use of motivational interviewing by health care providers has demonstrated effectiveness in eliciting behavioral change, leading to improved health outcomes. This paper aims to explore the concept and process of motivational interviewing and its role in facilitating behavior change in the plastic surgery setting. Methods: The authors reviewed the literature on motivational interviewing in various health care settings. Results: Motivational interviewing, first developed in the field of psychology, has demonstrated effectiveness in facilitating behavior change in various clinical contexts, including brief clinical encounters. Using motivational interviewing guides the patient as they move through the stages of readiness for change in dealing with unhealthy behaviors. The authors demonstrate these techniques in a supplemental instructional video. Conclusions: Motivational interviewing is an evidence-based method for facilitating behavior change. All plastic surgeons should be prepared to use this person-centred counselling method in clinical practice.

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)0.071
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.071
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.338
GPT teacher head0.414
Teacher spread0.076 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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