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Record W4311445138 · doi:10.32920/21728201.v1

The Impact of Walking Programs on Reduction of Menopausal Symptoms: A Systematic Review

2022· review· en· W4311445138 on OpenAlexaff
Hasina Amanzai, Souraya Sidani, Kaitlyn Munro, Wareesha Nadeem, Hanniya Ansari, Tooba Zia, Sepali Guruge

Bibliographic record

Venuenot available
Typereview
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMenopauseMedicineIntervention (counseling)GerontologyPhysical activityPhysical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

<p><strong>Aim: </strong>To identify types of walking programs and to determine the effects of these programs on menopausal symptoms. </p> <p><strong>Background: </strong>Menopause is a naturally occurring phenomenon for women and can present with several physical and mental symptoms that are more severely experienced by some individuals. These experiences can be very distressing for women to deal with, especially because of their personal, social, and work lives. The substantial impact of menopausal symptoms direly calls for effective intervention. Specific to menopause, physical activity is a form of intervention that can decrease occurrences and severity of menopausal symptoms. Previous studies have reported the benefits of walking programs in reducing symptoms, yet it is unclear which specific ones are most effective in reducing menopausal symptoms, especially because of the time and efficacy barriers reported by menopausal women. </p> <p><strong>Methods: </strong>A systematic review was conducted in nine databases for articles published since September 2011, to identify quantitative studies that evaluated walking programs in menopausal women. Two investigators independently screened articles extracted data related to the type and the outcomes of walking programs, and assessed risk of bias. Effect sizes were calculated to quantify the effects of walking programs on menopausal symptoms. </p>

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)0.011
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.283
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.165
GPT teacher head0.556
Teacher spread0.391 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations0
Published2022
Admission routes1
Has abstractyes

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