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Record W3175894280 · doi:10.32598/bcn.2021.2238.1

Psychometric Evaluation of Self-assessment Persian Version of the Alzheimer Questionnaire (AQ)

2021· article· en· W3175894280 on OpenAlexaboutno aff
Mahsa Roozrokh Arshadi Montazer, Roohollah Zahediannasab, Mohammad Nami, Mahshid Tahamtan, Roxana Sharifian, Mahdi Nasiri

Bibliographic record

VenueBasic and Clinical Neuroscience Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersShiraz UniversityShiraz University of Medical Sciences
KeywordsContent validityFace validityConvergent validityPsychologyReceiver operating characteristicPersianMontreal Cognitive AssessmentConstruct validityPearson product-moment correlation coefficientConcurrent validityPsychometricsInternal consistencyCognitive impairmentClinical psychologyCognitionStatisticsPsychiatryMathematicsTheology

Abstract

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Introduction: Mild cognitive impairment (MCI) is a primary disorder intensified by aging. Rapid diagnosis of MCI can prevent its progression towards the development of dementia. Thus, the present study was conducted to evaluate the psychometric features of the self-assessment Persian version of the Alzheimer questionnaire (AQ) in the elderly to detect MCI. Methods: First, the AQ was translated into the Persian language; then, its content validity was evaluated by the content validity index (CVI) and content validity ratio (CVR) method, and face validity was determined by two checklists for expert panel and the elderly. The convergent validity of the self-assessment AQ with the Montreal cognitive assessment (MoCA) was assessed using the Pearson correlation. The test-retest and internal consistency reliability were evaluated using intra-class correlation (ICC) and Kuder-Richardson coefficients, respectively. Moreover, the receiver operating characteristic curve was used to determine the optimal cut-off point of self-assessment AQ. Among 148 older people who took part in this study, 93 met our inclusion criteria (aged 60 years old or older, had reading and writing skills, and were able to speak and communicate). Results: A translated version of the questionnaire was named "M-check." The developed test showed good content and face validity. Statistically significant correlations were found between M-check and MoCA (r=-0.83, P<0.05). The Kuder-Richardson and ICC coefficients were obtained as 0.84 and 0.92, respectively. Area under the curve presented satisfactory values (Area under curve [AUC]=0.852, sensitivity=0.62, specificity=0.94). Conclusion: The M-check can be used as a valid and reliable instrument for assessing cognitive state and screening MCI in older adults. Highlights: All questions achieved desired face validity.The convergent validity of Alzheimer Questioner (AQ) was confirmed with high correlation.The AQ is statistically significant with Montreal Cognitive Assessment (MoCA).The AQ had acceptable stability, repeatability, and reliability.All findings demonstrated that the M-Check had high values in predicting MCI in the early stages. Plain Language Summary: Mild cognitive impairment (MCI) is a subset of mental disorders that is an early condition that may lead to dementia. People with MCI are usually prone to forgetfulness in a short time. If MCI is not detected in the early stages, it can progress to dementia or Alzheimer's to higher degrees. On the other hand, cognitive decline and MCI can cause major problems for patients and their families. So it is essential to act out as soon as possible. It is considered that a tool for the early identification of MCI that is self-assessed by individuals, without the presence of an expert and trained person to interpret the results, was not observed in Iran. Thus, the present study was conducted to evaluate the psychometric features of the self-assessment Persian version of the Alzheimer questionnaire (AQ) in the elderly. The results showed that the AQ is a simple one that can be quickly completed by any person at home or by family members of the elderly so that people can refer to the relevant specialist more soon if needed.

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 score0.057
Threshold uncertainty score0.148

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.088
GPT teacher head0.455
Teacher spread0.367 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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