MétaCan
Menu
Back to cohort
Record W4407387427 · doi:10.1093/ijnp/pyae059.420

ANALGESIC AVOIDANCE IN JAPAN: AN EPIDEMIOLOGICAL STUDY EXPLORING ATTITUDES TOWARD MENSTRUAL PAIN AND MEDICATION

2025· article· en· W4407387427 on OpenAlexaff
Keiko Yamada, Sakiko Yamaguchi, Naoki Mizunuma, Takashi Takeda

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldMedicine
TopicMenstrual Health and Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsAnalgesicEpidemiologyMedicinePsychologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background In Japan, 'addiction' is highly stigmatized, leading to the avoidance of analgesics due to concerns about addiction (Purnell 2019). Furthermore, there is a cultural belief in Japan that emphasizes enduring pain as a virtue (Purnell 2019). These cultural contexts related to pain contribute to attitudes toward analgesics in Japan. Aims & Objectives This study aimed to investigate the situation among Japanese women who do not use any analgesics for menstrual pain, concerning their avoidance of analgesics and fear of dependence. Additionally, the study aimed to examine the pain endurance practices among women who engage in self-medication for menstrual pain using over-the-counter (OTC) analgesics. Method All participants provided their web-based informed consent before responding to the online questionnaire. The Ethics Committee at Juntendo University Faculty of Medicine approved this study (approval number E22-0469-M02). We analyzed data from a cross-sectional web survey with self- reported questionnaires conducted in 2023 among Japanese women experiencing menstruation. The study included 557 women who did not use analgesics and 1284 women who exclusively used OTC analgesics for menstrual pain, after excluding potential confounding factors and other relevant variables. For women who did not use analgesics, we asked about their concerns regarding menstrual pain, reasons for not using pain relievers, and other related questions. For women who used OTC analgesics, we used a numerical rating scale (NRS) to assess pain threshold they deemed acceptable without analgesics and explored other pertinent factors. Results Of 557 women who did not use analgesics, 365 women (65.5%) were actually concerned with menstrual pain. For the 365 women who were concerned about menstrual pain but did not use analgesics, the reasons for not using analgesics are as follows: 15.3% of the women believed they should endure the pain, 15.1% thought analgesics are harmful to their health, and 40.6% fear that they might become dependent on them. The median number of analgesics was 2.0. Of the 1284 women who used analgesics, 47.1% believed that they should endure menstrual pain up to an NRS of 5 or higher, while 4.1% believed they should endure it up to an NRS of 8 or higher. Discussion & Conclusion This study represents the first epidemiological research in Japan that investigates the reasons behind analgesic avoidance for menstrual pain and the belief that one should endure pain without using analgesics for menstrual pain. Surprisingly, among women who actually experience concerns about menstrual pain, approximately 40% refrain from using analgesics due to fear of potential dependence. Additionally, about half of those who use analgesics for menstrual pain believe that they should only use them when their pain reaches an NRS of 5 or higher. It is important to suggest that excessive fear of using analgesics for menstrual pain is unnecessary, and that using analgesics appropriately can make menstrual discomfort more manageable. References Purnell, L.D. and Fenkl, E.A. (2019). People of Japanese heritage. In: Purnell, L. (ed.), Handbook for Culturally Competent Care, 1st edn. Springer Nature, New York.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.104
GPT teacher head0.439
Teacher spread0.335 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe International Journal of NeuropsychopharmacologySame topicMenstrual Health and DisordersFrench-language works237,207