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Record W3021812726 · doi:10.1186/s12955-020-01364-6

Is the Patient Activation Measure a valid measure of osteoarthritis self-management attitudes and capabilities? Results of a Rasch analysis

2020· article· en· W3021812726 on OpenAlexaff
Jillian Eyles, Manuela L. Ferreira, Kathryn Mills, Barbara R. Lucas, Sarah R. Robbins, Michael Williams, H. Lee, S. Appleton, David J. Hunter

Bibliographic record

VenueHealth and Quality of Life Outcomes · 2020
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsAlberta Bone and Joint Health Institute
Fundersnot available
KeywordsRasch modelDifferential item functioningRespondentPsychologyPsychometricsItem response theorySeparation (statistics)Polytomous Rasch modelClinical psychologyStatisticsPhysical therapyMedicineDevelopmental psychologyMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: The Patient Activation Measure (PAM-13) was developed using Rasch analysis to assess knowledge, skills and confidence in the management of one's health. Previous studies report positive relationships between PAM-13 scores, self-management behaviours and longitudinal health outcomes in adults with chronic disease. There is little extant measurement property evidence for the use of PAM-13 in specific osteoarthritis (OA) populations. This study tested measurement properties of the PAM-13 in people living with hip and knee OA. METHODS: Item response frequency analysis was conducted. Rasch analysis evaluated the fit of the PAM-13 data to the Rasch model. Model-data fit was evaluated using infit and outfit statistics; person/item reliability and person separation indices were computed. Unidimensionality was evaluated using Principal Components Analysis of Rasch residuals and the data were assessed for item redundancy. Differential Item Functioning (DIF) examined bias in respondent subgroups and correlations tested relationships between PAM-13 and other patient-reported outcomes. RESULTS: Two-hundred-and-seventeen PAM-13 surveys were completed; there were no missing responses, floor or ceiling effects. Person and item reliability were acceptable (0.98 and 0.87 respectively) with good separation (person separation index 2.58). Unidimensionality was evaluated, with 49.4% of the variance explained by the first eigenvector. There was evidence of potential local response-dependence. The Rasch fit statistics were acceptable (except for item-2). There were some issues identified with targeting of the PAM-13 items to people with higher ability and the item difficulty order was different to that proposed in original cohorts. Significant DIF was identified for sex and educational level for a small number of items. PAM-13 scores were moderately correlated with depressive symptoms on the Depression Anxiety Stress Scale and Assessment of Quality of Life-6D. There were small correlations between PAM-13 and Knee injury and Osteoarthritis Outcome Score pain and activities of daily living scores. CONCLUSIONS: This study provides some evidence of adequate person and item reliability, unidimensionality, and construct validity to support the use of PAM-13 to measure patient activation in people living with hip and knee OA. Possible limitations regarding targeting, different item difficulty order, DIF and local response dependence should be investigated in future research.

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)0.037
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.093
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.337
Teacher spread0.269 · 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 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

Citations43
Published2020
Admission routes1
Has abstractyes

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