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Record W2808884192 · doi:10.1111/cch.12577

Measure of processes of care (MPOC): Translation and validation for use in Korea

2018· article· en· W2808884192 on OpenAlexaboutno aff
Mihee An, Jun-Hyoung Kim

Bibliographic record

VenueChild Care Health and Development · 2018
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsnot available
Fundersnot available
KeywordsReliability (semiconductor)Cronbach's alphaIntraclass correlationConstruct validityScale (ratio)PsychologyTest (biology)ValidityClinical psychologyPhysical therapyMedicineApplied psychologyGeographyCartographyPsychometricsPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: The measure of processes of care (MPOC) is a widely used instrument to assess parents' perception of the extent to which healthcare services they and their child receive are family centred. The purpose of this study was to examine the reliability and validity of the Korean translation of the MPOC (Korean MPOC). METHODS: The Korean MPOC was completed by 198 parents of children receiving rehabilitation services in five provinces in South Korea. According to the Canadian validation procedures, analyses for internal consistency, construct and concurrent validity, and test-retest reliability were performed. RESULTS: The Korean MPOC demonstrated adequate internal consistency, with Cronbach's alpha ranging from .85 to .98. Confirmative analyses of the scale structure support the construct validity of the Korean MPOC. The Pearson correlations r between the MPOC scale scores and Client Satisfaction Inventory score ranged from .60 to .83, supporting the concurrent validity of the Korean MPOC. The intraclass correlation coefficients were greater than .80 for all five scales, demonstrating good test-retest reliability. CONCLUSIONS: The Korean MPOC has good psychological properties and can be recommended for evaluation of processes of paediatric rehabilitation in Korea.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.102
GPT teacher head0.382
Teacher spread0.280 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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