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Record W2883628339 · doi:10.2147/ppa.s165749

Self-efficacy for medication management: a systematic review of instruments

2018· review· en· W2883628339 on OpenAlexafffund
Larkin Lamarche, Ambika Tejpal, Dee Mangin

Bibliographic record

VenuePatient Preference and Adherence · 2018
Typereview
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsMedicineMEDLINEConstruct validityScale (ratio)PsychometricsReliability (semiconductor)Systematic reviewConstruct (python library)Clinical psychologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Medication self-efficacy is a potentially important construct in research around optimal use of prescription medications. A number of medication self-efficacy measures are available; however, there is no systematic review of existing instruments and cataloguing of their theoretical underpinnings or psychometric properties, strengths, and weaknesses. The aim of the study was to identify instruments that measure self-efficacy for medication management. The study also aimed to examine the quality, theoretical grounding, and psychometric evaluation of existing measures of self-efficacy for medication management. The study was a systematic review. METHODS: Data were extracted from PubMed, OVID, and MEDLINE using a predefined search strategy. Citations were included if they reported the development and/or psychometric evaluation of an instrument to measure self-efficacy for medication management and were in English. Abstracts were screened for studies potentially meeting eligibility criteria. Full articles of these studies were then reviewed in depth. The review was carried out independently by two members of the research team. RESULTS: The search identified 158 citations of which 12 were included after screening. Full review identified 3 articles fitting inclusion criteria for the review. Generally, development was theoretically grounded and included patients and experts in the field. Psychometric testing showed evidence of internal consistency (2/3 instruments) and test-retest reliability (1/3 instruments). All instruments showed some validity; however, assessment of all forms of validity for each instrument was lacking. CONCLUSION: Although our analysis would recommend the use of the Self-Efficacy for Appropriate Medication Use Scale because of the current evidence of validity and reliability, more psychometric evaluation is required, particularly in terms of responsiveness to change as self-efficacy is a malleable patient-level factor. Three measures of self-efficacy for medication management were identified. Overall, some evidence of reliability and/or validity was demonstrated for all instruments; however, other forms of validity were not tested (ie, responsiveness to change). Use of a well-validated measure of self-efficacy medication management is essential in order to understand relationships between medication self-efficacy and other patient-reported outcomes such as patient-centeredness, patient enablement, and burden of treatment, an important area of research that is currently lacking.

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)0.020
metaresearch head score (Gemma)0.084
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.084
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0180.018
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.127
GPT teacher head0.367
Teacher spread0.240 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations53
Published2018
Admission routes2
Has abstractyes

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