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Record W4379742157 · doi:10.47626/1679-4435-2023-1064

Low back pain in beauty salons professionals in the city of Fortaleza-CE

2023· article· en· W4379742157 on OpenAlexaboutno aff
Paulo Ricardo Silva Lopes, Giulianna de Brito Brasil, Thiago Brasileiro de Vasconcelos

Bibliographic record

VenueRevista Brasileira de Medicina do Trabalho · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsBeautyBusinessArtAesthetics

Abstract

fetched live from OpenAlex

Introduction: Low back pain can be defined as pain below the ribs and above the upper gluteal line. Objectives: The study aimed to analyze low back pain in professionals from beauty salons in the city of Fortaleza, state of Ceará. Methods: Descriptive, quantitative-qualitative, transversal, non-probabilistic research in the snowball modality, conducted between June and August 2021 in the José Walter neighborhood. Two sociodemographic questionnaires and the Quebec Back Pain Disability scale were applied, which seeks to assess how pain affects the participants' daily lives. Results: Forty-two professionals were interviewed, of which 32 women (76.2%), with a mean age of 39.45 ± 10.99 years. Women were more likely to have an onset of low back pain and to live with pain for a longer time compared to men, in addition to these professionals having a significant overload for the hours worked. 52% of respondents showed significant clinical changes, mainly in relation to stand up for 20-30 minutes (16.7%), sit in a chair for several hours (14.3%), walk several kilometers (19%), carry two bags with groceries (14.3%) and lift and carry a heavy suitcase (28.6%). Conclusions: It was evidenced that low back pain may be related to personal or environmental factors, with a sedentary lifestyle, length of service and working hours as strong indications for the onset of low back pain, with impairment in daily tasks.

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)0.012
metaresearch head score (Gemma)0.002
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 score0.019
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
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 head0.081
GPT teacher head0.443
Teacher spread0.362 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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