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Record W4300773409 · doi:10.24926/iip.v13i3.4934

Development of a Propensity to Self-Medicate with Over-the-Counter Medicines Scale (PSM-OTC)

2022· article· en· W4300773409 on OpenAlexaff
Oluwasola Stephen Ayosanmi, Marjorie Delbaere, Jeff Taylor

Bibliographic record

VenueINNOVATIONS in pharmacy · 2022
Typearticle
Languageen
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCronbach's alphaIntraclass correlationReliability (semiconductor)Scale (ratio)Construct validityPsychologyTest (biology)Rating scalePearson product-moment correlation coefficientCorrelationStatisticsClinical psychologyMedicinePsychometricsMathematics

Abstract

fetched live from OpenAlex

Objective: To develop a valid and reliable scale to measure the public’s propensity to self-medicate with OTC medicines. Method: Propensity construct items were obtained from the literature and also created as new entities. Three experts reviewed the item pool for face validity. Internal consistency was assessed using Cronbach’s alpha. Test-retest reliability was estimated using Pearson correlation coefficients (r), Intraclass Correlation Coefficients (ICC) and paired sample t-tests. Further test-retest reliability assessed the degree of change in responses in a subset of subjects from time 1 to time 2 (one month apart) for each item on the scale. Results: From the pool, 16 items were assessed for applicability to the propensity construct. Factor Analysis identified four components and were labelled as purchase involvement, self-efficacy, awareness of care needed during self-medication, and the therapeutic usefulness of OTC medicines. The internal consistency of the 16-item scale was sufficient; overall alpha was 0.9 and each construct had an alpha of 0.7 to 0.8. Test-retest reliability coefficients (r) for the four components were reassuring, ranging from 0.4 to 0.5, while the ICC values ranged from 0.5 to 0.7. A paired sample t-test showed no statistically significant difference in the rating at the two iterations for each of the constructs, thereby suggesting good reliability of the data. Over 50% of respondents did not change their original response to the 7-point scales (strongly disagree (1) to strongly agree (7)) for 9 out of 16 items. Factor loading from Principal Component Analysis led to the reduction of the 16-items scale to a 15-item Propensity to Self-Medicate with OTC Medicines Scale. Conclusion: The developed tool for measuring the propensity to self-medicate with OTC medicines showed acceptable performance of internal consistency and reliability. The scale may have research potential in assessing the self-medication propensity of different cohorts of society.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.110
GPT teacher head0.383
Teacher spread0.273 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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