MétaCan
Menu
Back to cohort
Record W2385121569 · doi:10.4103/0300-1652.182070

Pedometer-determined physical activity profile of healthcare professionals in a Nigerian tertiary hospital

2016· article· en· W2385121569 on OpenAlexaff
Oluwatoyosi B. A. Owoeye, Adetipe Tomori, Sunday Akinbo

Bibliographic record

VenueNigerian Medical Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicinePedometerHealth professionalsHealth carePhysical therapyTertiary careTertiary levelPhysical activityFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Healthcare professionals (HCPs) are perceived as statutory advocates for healthy living and promotion of healthy behaviors such as regular participation in physical activity (PA). This study assessed and compared pedometer-determined PA of different urban HCPs in a Nigerian tertiary hospital. MATERIALS AND METHODS: A cross-sectional study involving 180 HCPs from a tertiary hospital in Lagos, Nigeria. PA was measured by daily walking steps using a pedometer. RESULTS: The mean step count obtained was 7,396.94 ± 2,714.63 steps/day. Only 20% of the HCPs met a minimum PA of 10,000 steps/day. About one-third (34.4%) of the HCPs were low active and less than a quarter (23.9%) were somewhat active. Further, less than half (43.9%) of the HCPs were found to have PA levels ≥7,500 steps/day. Overall, nurses had the highest step counts (7,980 steps/day) followed by physiotherapists (7,332 steps/day), while pharmacists had the lowest step counts (6,201 steps/day). There was however no significant difference in the mean step counts of the various cadres of the HCPs (P > 0.05). Step counts of HCPs were found to significantly negatively correlate with their age (r = -0.53; P < 0.001), body mass index (r = -0.39; P < 0.001), and body fat percentage (r = -0.42; P < 0.001). CONCLUSION: PA profile of the HCPs was mostly characterized by a low active PA level and less than a quarter met the recommended minimum of 10,000 steps/day.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.348
Teacher spread0.329 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueNigerian Medical JournalSame topicPhysical Activity and HealthFrench-language works237,207