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Record W4306681807 · doi:10.1016/j.jmh.2022.100140

Physical inactivity among internally displaced persons in Nigeria

2022· article· en· W4306681807 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Migration and Health · 2022
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of LethbridgeWestern UniversityUniversity of Manitoba
Fundersnot available
KeywordsInternally displaced personLogistic regressionMedicineConfidence intervalPhysical activityDemographyPhysical healthGerontologyMental healthEnvironmental healthPopulationPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Background: Physical inactivity may complicate physical and mental health problems among internally displaced persons (IDPs). This study aimed to assess the prevalence of physical inactivity and its sociodemographic correlates among IDPs in Northeastern Nigeria. Methods: A total of 363 participants recruited from four IDP camps were categorized into physically inactive and active using International Physical Activity Questionnaire. Multiple logistic regression was used to explore the association between physical inactivity and sociodemographic correlates. Results: The prevalence of physical inactivity was 36.2%. Those who were males (Odd Ratio (OR) = 4.52, 95% Confidence Interval (CI) = 2.33 to 8.78) and Kanuri (OR = 2.53, 95% CI = 1.44 to 4.45) were more likely to be physically inactive. Younger participants were less likely to be physically inactive than those who were aged >49 years old. Conclusion: There is a high prevalence of physical inactivity among IDPs in Nigeria, and we found important sociodemographic factors associated with physical inactivity.

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.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.263

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.038
GPT teacher head0.361
Teacher spread0.323 · 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