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Record W7133069628

How Shall We Measure Participation: Patient Preferences

2025· dissertation· W7133069628 on OpenAlexaff
Benjamin Yair Traubici

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMeasure (data warehouse)Patient participationPreferenceHealth professionalsQuality of life (healthcare)MEDLINE
DOInot available

Abstract

fetched live from OpenAlex

Background – “Participation” is defined as “involvement in life situations” by the World Health Organization, in their International Classification of Functioning, Disability and Health (ICF). No one yet knows whether a patient-reported or patient-centred approach is preferred for measuring participation. Hypothesis – Patient-centred questionnaires are preferred to patient-reported. Methods – Eighty participants, aged 9 to 17, were recruited from the SickKids rheumatology clinic. After completion of three questionnaires participants scored each based on how satisfied they were with how it allowed them to express their participation. Results – We found no evidence that one questionnaire was preferred over the others. Patient-Centred Participation Questionnaire (PCPQ) scores were normally distributed and did not correlate well with other questionnaire scores. Participant comments showed questionnaire likes and dislikes of participants. Conclusions – There is a role for the PCPQ because it gives us different information, is mathematically better distributed, and participants like it just as much as traditional tools.

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)0.038
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.203

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.100
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.052
GPT teacher head0.341
Teacher spread0.289 · 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 designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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