Evidence map›Paper›PMID 41913754›Full record

ArticleJournal of multidisciplinary healthcare2026

Psychometric Properties of the Turkish Medical Maximizer-Minimizer Scale.

Yusuf Celik, Ecenur Aydemir, Muhammed Emre Güvey, Ahmet Can Kucukkurt, Mesut Cimen, Salim Yılmaz

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Article in Journal of multidisciplinary healthcare, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yusuf CelikDepartment of Health Management, Graduate School of Health Sciences, Acibadem Mehmet Ali Aydınlar University, Istanbul, Türkiye.
Ecenur AydemirDepartment of Health Management, Faculty of Health Sciences, Acibadem Mehmet Ali Aydınlar University, Istanbul, Türkiye.
Muhammed Emre GüveyDepartment of Health Management, Faculty of Health Sciences, Acibadem Mehmet Ali Aydınlar University, Istanbul, Türkiye.
Ahmet Can KucukkurtDepartment of Health Management, Graduate School of Health Sciences, Acibadem Mehmet Ali Aydınlar University, Istanbul, Türkiye.
Mesut CimenDepartment of Health Management, Graduate School of Health Sciences, Acibadem Mehmet Ali Aydınlar University, Istanbul, Türkiye.
Salim YılmazDepartment of Health Management, Graduate School of Health Sciences, Acibadem Mehmet Ali Aydınlar University, Istanbul, Türkiye.ORCID 0000-0003-2405-5084

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: This study aimed to adapt the Medical Maximizer-Minimizer Scale (MMS) into Turkish and evaluate its psychometric properties, including factorial structure, reliability, measurement invariance, and construct validity in the Turkish healthcare context. Patients and Methods: A total of 511 Turkish adults completed the Turkish MMS alongside measures of healthcare trust, utilization patterns, and health-related quality of life. The sample was divided into exploratory (n = 256) and confirmatory (n = 255) subsamples using propensity score matching. Classical item analysis, exploratory graph analysis (EGA), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) were conducted to examine dimensional structure. Five competing structural models were compared, followed by measurement invariance testing across demographic groups. Convergent and discriminant validity were evaluated through correlations with related constructs. Results: Item analysis and EGA revealed that Item 5 demonstrated poor psychometric properties and was excluded, yielding a nine-item scale. EGA identified a stable two-dimensional structure with high bootstrap stability coefficients (0.946-1.000). The nine-item scale demonstrated good internal consistency (α =0.78, 95% CI [0.74,0.82]). Among five competing models, the bifactor model provided superior fit (CFI =0.997, TLI =0.993, RMSEA =0.021, SRMR =0.025), identifying a general maximizing-minimizing factor alongside two specific dimensions: Generalist View (philosophical orientations toward medical intervention) and Individualist View (concrete healthcare-seeking preferences). Scalar measurement invariance was supported across gender, age, marital status, parental status, and social security status. The scale showed theoretically consistent correlations with healthcare trust (r =0.15-0.25) and utilization intentions (r =0.09-0.29), but minimal associations with health-related quality of life, confirming discriminant validity. Conclusion: The nine-item Turkish MMS demonstrates robust psychometric properties with a bifactor structure capturing both general and dimension-specific healthcare orientations. The validated instrument provides a reliable tool for assessing maximizing-minimizing tendencies in Turkish populations and offers a foundation for future research on healthcare utilization and patient-centered communication.

Indexed as

bifactor modelhealthcare utilizationmeasurement invariancemedical maximizer-minimizer scalepsychometric validationTurkish adaptation

Identifiers

PMID41913754
PMCPMC13033267

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