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Towards a working-definition of patient-centred Measurement: A review of literature

2023· other· en· W6977707490 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Meaning (existential)Work (physics)Value (mathematics)Cluster (spacecraft)Focus (optics)

Abstract

fetched live from OpenAlex

Between March and June, 2018 a review of academic and grey literature was conducted to explore the meaning of patient-centred measurement (PCM). The research question guiding the review was: “How has patient-centred measurement been defined?” This report presents the findings arising from the review with the goal of arriving at a working definition of PCM to inform plans and activities that will be undertaken by the British Columbia SUPPORT Unit’s Patient-Centred Measurement Methods Cluster (the PCM Methods Cluster). By targeting the focus of the PCM Methods Cluster, the value of the research and application work to be supported by the Methods Cluster will be maximized and will fulfill the overarching goal of establishing evidenced-based methods related to patient-centred measurement in the context of the Canadian Institute for Health Research’s Strategy for Patient-oriented Research (SPOR).

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)0.097
metaresearch head score (Gemma)0.186
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.097
Threshold uncertainty score0.512

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0970.186
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0390.048
Science and technology studies0.0030.007
Scholarly communication0.0140.017
Open science0.0050.007
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0040.002

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 head0.146
GPT teacher head0.277
Teacher spread0.131 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2023
Admission routes1
Has abstractyes

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