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Record W4411633612 · doi:10.1016/j.jht.2025.04.008

The MacHAND performance assessment (MPA): Development and psychometric testing of the short English version (MPA-S) for the traumatic hand injury population

2025· article· en· W4411633612 on OpenAlexafffundabout
Zoë Edger-Lacoursière, Valérie Calva, Ingrid Malo Leclerc, Elisabeth Marois-Pagé, Geneviève Schneider, Danielle Shashoua, Chloé Tremblay, Ariane Vaillancourt, Tara Packham, Alia Sajjad, José A. Correa, Sara Ahmed, Bernadette Nedelec

Bibliographic record

VenueJournal of Hand Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsMcMaster UniversityCentre Hospitalier de l’Université de MontréalMcGill University
FundersRéseau Provincial de Recherche en Adaptation-RéadaptationFondation des pompiers du Québec pour les grands brûlés
KeywordsPsychologyPopulationPsychometric testingMedicinePhysical therapyClinical psychologyPsychometrics

Abstract

fetched live from OpenAlex

BACKGROUND: Hand injuries are the most common work-related injuries in Canada, causing significant functional limitations, occupational performance issues, and delaying return to work. Standardized hand function measures are essential for guiding interventions, clinical decisions, and cost-effective care. However, few assessments have been developed for traumatic hand injuries, with even fewer being performance-based outcome (PerfOs) assessments. The MacHAND performance assessment (MPA) is a PerfO developed to evaluate hand function in the traumatic hand injury population. PURPOSE: To revise the MPA instructions and scoring manual (MPA 2.0), produce a shortened version (MPA-S), and evaluate its psychometric properties in adults with traumatic hand injuries. STUDY DESIGN: Mixed method. METHODS: The original MPA instruction's and scoring manual was revised to ensure language consistency, update pictures, and provide identical 3D-printed versions where appropriate (MPA 2.0). To produce the MPA-S, a combined statistical and Delphi approach with rehabilitation experts was used. An existing dataset from the original MPA was then used to determine MPA-S internal consistency, test-retest, and inter-rater reliability for the traumatic hand injury population and agreement with the MPA. Evidence-based dissemination strategies were used to promote clinical and research adoption. RESULTS: For the MPA-S, 10 items were retained for dominant and eight items for nondominant hand testing. The MPA-S showed good internal consistency (Cronbach's alpha and 95% CI: dominant hand: 0.85 [0.78, 0.90], nondominant hand: 0.88 [0.79, 0.92]), excellent test-retest reliability (r = 0.97, 95% CI [0.85, 1]), and inter-rater reliability (ICC 0.98, 95% CI [0.97, 0.99]), and the mixed-effects limits of agreement plots show good agreement with the MPA. CONCLUSIONS: The MPA-S is one of the quickest task-based PerfOs to administer, uses many 3D-printed components, and everyday objects, making it highly accessible and affordable. We believe the MPA 2.0, the creation of the MPA-S, and targeted dissemination strategies will increase clinical and research uptake.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.330
Teacher spread0.300 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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