Health-Seeking Behaviors and Treatments Received for Menopause Symptoms: A Questionnaire Survey among Midlife Women Attending Primary Healthcare Clinics in Malaysia
Bibliographic record
Abstract
OBJECTIVES: This study aimed to assess menopause symptoms, treatment-seeking behaviors, treatments received, and factors associated with seeking consultation from healthcare providers (HCPs). METHODS: Using a self-administered Menopause Quick-6 in the Malay language (MQ6[M]) questionnaire, we surveyed 349 women aged 40-60 years attending primary healthcare clinics in four states in Malaysia for their menopause symptoms. Health-seeking behaviors for menopause symptoms were assessed using questions regarding HCPs consulted and treatments prescribed. Binary logistic regression was employed on factors associated with seeking consultation for menopause symptoms. RESULTS: Using MQ6(M), we observed that 125 (31.3%) women reported at least one menopause symptom, with joint pains (42.8%), menstrual changes (39.5%), and hot flashes (29.3%) being the most frequent symptoms. Furthermore, 60% of the women were prescribed vitamins, and only 13% were administered Hormone Replacement Therapy (HRT). Medical comorbidities, the presence of at least one gynecological condition, menopause status, and MQ6(M) score were associated with seeking consultation with an HCP. For women with medical conditions, the odds of seeking consultation increased by a factor of 1.34 (adjusted odds ratio [AOR], 1.34; 95% confidence interval [CI], 1.11-1.76) for every additional comorbidity. The odds of seeking consultation from an HCP increased by a factor of 1.26 (AOR, 1.26; 95% CI, 1.04-1.47) with a unit increase in MQ6(M) score. CONCLUSIONS: Most women had menopause symptoms but favored the use of complementary and alternative medicine over HRT. Screening and awareness of menopause treatments need to be improved at primary healthcare clinics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".