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Record W2166240186 · doi:10.5539/gjhs.v6n3p37

Non-Adherence to Medication in Outpatient Setting in Nigeria: The Effect of Employment Status

2014· article· en· W2166240186 on OpenAlexvenueno aff
Senanu Okuboyejo

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsnot available
Fundersnot available
KeywordsMarital statusMedicineMedication adherencePsychological interventionFamily medicinePsychiatryEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Non-adherence to prescribed medication and health regimen has been identified as responsible for poor health outcomes. This study investigates the reasons for medication non-adherence in outpatient setting among malaria patients in Nigeria. METHODS: This research adopted quantitative research methods. A well-structured questionnaire was completed by 440 respondents with minimum age of 18 years. The aim of the questionnaire was to get respondents' reasons for non-adherence to medication. The demographic details of the respondents were also captured. RESULTS: Age, gender, educational level, marital status and medication payment were found not to influence non-adherence while employment was a significant variable. Respondents also indicated fear of death, nauseating smell of drugs, religious beliefs, the side effects of medication, the fear of taking counterfeit drugs or drugs that are past their expiry dates as also responsible for non-adherence. CONCLUSION: The results highlighted reasons for poor adherence in southwest Nigeria. Interventions can be targeted towards these reasons.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.094
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.376
Teacher spread0.354 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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