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Record W4405089208 · doi:10.1186/s12909-024-06420-z

The effect of computer-based Stress Inoculation Training (SIT) approach on the pelvic pain, depression, and anxiety in students with primary dysmenorrhea: a clinical trial study

2024· article· en· W4405089208 on OpenAlexaboutno aff
Leila Dailer, Hajar Adib‐Rad, Fatemeh Bakouei, Mahbobeh Faramarzi, Soraya Khafri

Bibliographic record

VenueBMC Medical Education · 2024
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsnot available
FundersBabol University of Medical Sciences
KeywordsAnxietyMcGill Pain QuestionnairePhysical therapyDistressMedicineDepression (economics)Perceived Stress ScaleChecklistHospital Anxiety and Depression ScalePelvic painClinical psychologyRepeated measures designPsychologyStress (linguistics)PsychiatryVisual analogue scaleSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Primary dysmenorrhea (PD) is one of the most common pains experienced by women. Stress Inoculation Training (SIT) is a cognitive-behavioral technique that immunizes them against future periods of stress. The purpose of this research is to investigate the effectiveness of a computer-based stress inoculation approach on pelvic pain, depression, and anxiety symptoms in students with PD. METHODS: The present study is a clinical trial conducted on 100 students with PD. The SIT intervention consisted of eight consecutive sessions. The content of the sessions was created as multimedia on a computer platform of a site. In each session, several interactive questions were asked about the topics of that session, and participants answered them, with responses recorded in the system. Then, each individual session, lasting about 50 min, was sent as a link (offline) to a participant. Data collection tools included the demographics Checklist, McGill Pain Questionnaire (MPQ), Moos Menstrual Distress Questionnaire (MMDQ), Hospital Anxiety and Depression Scale (HADS), Pain Self-Efficacy Questionnaire (PSEQ), and Perceived Stress Scale (PSS-14), which were completed on the second menstrual day of the cycle before the study and for three consecutive cycles after the intervention by each student. The data were analyzed using chi-square, generalized linear mixed models (GLMM), and multiple linear regression tests. The significance level was set at P < 0.05. RESULTS: The results of the GLMM test showed that the SIT intervention decreased depression (p = 0.002), anxiety (p = 0.001), menstrual distress (p = 0.001), pain intensity (p = 0.003), and perceived stress (p = 0.002), while it increased pain self-efficacy (p = 0.001). Based on multiple linear regression analysis, the main predictors of depression were the student's age and residence in a dormitory (β=-0.255, p = 0.047 and β=-0.376, p = 0.005, respectively). The factors influencing pain self-efficacy were birth rank and the type of university admission (β=-0.351, p = 0.027 and β=-0.249, p = 0.030, respectively). In the presence of confounding variables, increasing age and living in a dormitory were risk factors that increased depression, while studying with tuition fees and a higher birth rank were associated with decreased self-efficacy. CONCLUSION: The computer-based SIT was effective in reducing pelvic pain, and psychological factors, and in increasing pain self-efficacy in students with primary dysmenorrhea. Therefore, it can be used as a useful solution to manage complications associated with primary dysmenorrhea. TRIAL REGISTRATION: IRCT20230130057274N5 Date of registration: 2024-04-29 Retrospectively registered.

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)0.007
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 score0.299
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
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 integrity0.0000.000
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 head0.042
GPT teacher head0.409
Teacher spread0.366 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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