Establishing a framework of measurement for use in Long COVID research and practice: protocol for a scoping review involving evidence review and consultation
Bibliographic record
Abstract
INTRODUCTION: Our aim is to develop a Framework of Measurement for people living with Long COVID and their caregivers for use in Long COVID research and clinical practice. Specifically, we will characterise evidence pertaining to outcome measurement and identify implementation considerations for use of outcome measures among adults and children living with Long COVID and their caregivers. METHODS AND ANALYSIS: We will conduct a scoping study involving: (1) an evidence review and (2) a two-phased consultation, using methodological steps outlined by the Arksey and O'Malley Framework and Joanna Briggs Institute. We will answer the following question: What is known about outcome measures used to describe, evaluate or predict health outcomes among adults and children living with Long COVID and their caregivers? EVIDENCE REVIEW: we will review peer review published and grey literature to identify existing outcome measures and their reported measurement properties with people living with Long COVID and their caregivers. We will search databases including MEDLINE, Embase, CINAHL, PsycINFO and Scopus for articles published since 2020. Two authors will independently review titles and abstracts, followed by full text to select articles that discuss or use outcome measures for Long COVID health outcomes, pertain to adults or children living with Long COVID and/or their caregivers and are based in research or clinical settings. We will extract data including article characteristics, terminology and definition of Long COVID, health outcomes assessed, characteristics of outcome measures, measurement properties and implementation considerations. We will collate and summarise data to establish a preliminary Framework of Measurement. Consultation phase 1: we will conduct an environmental scan involving a cross-sectional web-based questionnaire among individuals with experience using or completing outcome measures for Long COVID, to identify outcome measures not found in the evidence review and explore implementation considerations for outcome measurement in the context of Long COVID. Consultation phase 2: we will conduct focus groups to review the preliminary Framework of Measurement and to highlight implementation considerations for outcome measurement in Long COVID. We will analyse questionnaire and focus group data using descriptive and content analytical approaches. We will refine the Framework of Measurement based on the focus group consultation using community-engaged approaches with the research team. ETHICS AND DISSEMINATION: Protocol approved by the University of Toronto Health Sciences Research Ethics Board (protocol #46503) for the consultation phases of the study. Outcomes will include a Framework of Measurement, to enhance measurement of health outcomes in Long COVID research and clinical practice. Knowledge translation will also occur in the form of publications and presentations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.362 | 0.324 |
| Meta-epidemiology (narrow) | 0.008 | 0.009 |
| Meta-epidemiology (broad) | 0.015 | 0.022 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.012 | 0.019 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.062 | 0.023 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".