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Record W2063658089 · doi:10.1097/brs.0b013e3182579795

Development of the Italian Version of the Neck Disability Index

2012· article· en· W2063658089 on OpenAlexaff
Marco Monticone, Simona Ferrante, Howard Vernon, Barbara Rocca, Fulvio Dal Farra, Calogero Foti

Bibliographic record

VenueSpine · 2012
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsCanadian Memorial Chiropractic College
Fundersnot available
KeywordsCronbach's alphaIntraclass correlationMedicineConstruct validityPhysical therapyHospital Anxiety and Depression ScaleAnxietyRating scaleExploratory factor analysisReliability (semiconductor)Neck painPsychometricsClinical psychologyScale (ratio)PsychiatryPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

STUDY DESIGN: Evaluation of the psychometric properties of a translated and culturally adapted questionnaire. OBJECTIVE: Translating, culturally adapting, and validating the Italian version of the Neck Disability Index (NDI-I) to allow its use with Italian-speaking patients with neck pain (NP). SUMMARY OF BACKGROUND DATA: More attention is being given to standardized outcome measures to improve interventions for NP. A translated form of the NDI has never been validated in Italian patients with NP. METHODS: The NDI-I was developed by forward-backward translation, a final review by an expert committee, and a test of the prefinal version to establish its correspondence with the original English version. The psychometric testing included factor analysis, reliability by internal consistency (Cronbach α) and test-retest reliability (intraclass coefficient correlation), construct validity by comparing NDI-I with the Neck Pain and Disability Scale, a numerical rating scale, the Hospital Anxiety and Depression Scale, and the 36-Item Short Form Health Survey (Spearman correlation), and sensitivity to change by calculating the smallest detectable change. RESULTS: The questionnaire was administered to 101 subjects with chronic NP and proved to be acceptable. Factor analysis revealed a 2-factor 10-item solution (explained variance: 56%). The questionnaire showed good internal consistency (α = 0.842) and test-retest reliability (intraclass coefficient correlation = 0.846). Construct validity showed a good correlation with Neck Pain and Disability Scale (ρ = 0.687), moderate correlations with the numerical rating scale (ρ = 0.545), and Hospital Anxiety and Depression Scale (ρ = 0.422 for the Anxiety score and ρ = 0.546 for the Depression score), and poor correlations with the 36-Item Short Form Health Survey subscales (ρ = 0.066 to -0.286). The psychometric analyses of the subscales and total scale were similar. The smallest detectable change of the NDI-I was 3. CONCLUSION: The NDI was successfully translated into Italian and proved to have a good factorial structure and psychometric properties that replicated the results of other versions. Its use is recommended for research purposes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.011
GPT teacher head0.273
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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

Citations74
Published2012
Admission routes1
Has abstractyes

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