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Record W2049243178 · doi:10.4103/0976-7800.122258

Validity and reliability of male andropause symptoms self-assessment questionnaire among elderly males in Khuzestan province of Iran

2013· article· en· W2049243178 on OpenAlexaff
Abdolrahim Asadollahi, LalehFani Saberi, Nasrin Faraji

Bibliographic record

VenueJournal of Mid-life Health · 2013
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsCronbach's alphaConfirmatory factor analysisGoodness of fitConvergent validityPsychologyStatisticsDemographyStatisticValidityScale (ratio)Exploratory factor analysisPsychometricsStructural equation modelingGerontologyClinical psychologyMathematicsMedicineInternal consistencyGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Andropause is a condition of decreasing testosterone in men that usually begins to occur at about 40 years of age. Many men find it difficult to acknowledge there may be a problem by refusing to even talk about the symptoms. AIMS: The study was conducted to the standards of MASSQ (2012) within male older adults to introduce a relevant criterion. MATERIALS AND METHODS: About 382 men with age range of 50-80 and with the mean age of 65.3 ± 2.32 were sampled with the cluster-ratio sampling method from the eight cities of Khuzestan province in southwestern Iran. The aged samples replied to the 25 items of MASSQ. RESULTS: Coefficients of Cronbach's alpha (α = 0.89), split-half (0.91), convergent validity (0.72), divergent validity (-0.32), and criterion validity (0.67) were estimated, which were significant at P < 0.01. The exploratory factor analysis demonstrated that the 25-items of MASSQ for aged samples are organized into four factors (sexual, somatic, psychic, and behavioral) which clarify 79% of the scale's variance. Second-order confirmatory factor analysis pointed out that the factors are well-matched up onto a principal factor. Consequently, the four-factor model was well appropriate for the data by the fit index techniques for adjusting the scale [adjusted goodness of fit index = 0.92, goodness-of-fit statistic = 0.91, root mean square error of approximation = 0.006, incremental fit index = 0.94, normed fit index = 0.91, comparative fit index = 0.97]. CONCLUSIONS: The results pointed to the well-adjusted reliability and validity of MASSQ and its usefulness for the relevant studies as well.

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)0.002
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 score0.005
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
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 head0.021
GPT teacher head0.313
Teacher spread0.293 · 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
Published2013
Admission routes1
Has abstractyes

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