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Record W3205389994 · doi:10.1186/s41687-021-00374-2

Selection of patient-reported outcome measures (PROMs) for use in health systems

2021· article· en· W3205389994 on OpenAlexaffabout
Fatima Al Sayah, Xuejing Jin, Jeffrey Johnson

Bibliographic record

VenueJournal of Patient-Reported Outcomes · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPromPatient-reported outcomeStandardizationScope (computer science)Context (archaeology)Health careComputer scienceMedicineNursingQuality of life (healthcare)Geography

Abstract

fetched live from OpenAlex

Many healthcare systems around the world have been increasingly using patient-reported outcome measures (PROMs) in routine outcome measurement to enhance patient-centered care and incorporate the patient's perspective in health system performance evaluation. One of the key steps in using PROMs in health systems is selecting the appropriate measure(s) to serve the purpose and context of measurement. However, the availability of many PROMs makes this choice rather challenging. Our aim was to provide an integrated approach for PROM(s) selection for use by end-users in health systems.The proposed approach was based on relevant literature and existing guidebooks that addressed PROMs selection in various areas and for various purposes, as well as on our experience working with many health system users of PROMs in Canada. The proposed approach includes the following steps: (1) Establish PROMs selection committee; (2) Identify the focus, scope, and type of PROM measurement; (3) Identify potential PROM(s); (4) Review practical considerations for each of the identified PROMs; (5) Review measurement properties of shortlisted PROMs; (6) Review patient acceptance of shortlisted PROMs; (7) Recommend a PROM(s); and (8) Pilot the selected PROM(s). The selection of appropriate PROMs is one step in the successful implementation of PROMs within health systems, albeit, an essential step. We provide guidance for the selection of PROMs to satisfy all potential usages at the micro (patient-clinician), meso (organization), and macro (system) levels within the health system. Selecting PROMs that satisfy all these purposes is essential to ensure continuity and standardization of measurement over time. This is an iterative process and users should consider all the available information from all presented steps in selecting PROMs. Each of these considerations has a different weight in diverse clinical contexts and settings with various types of patients and resources.

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)0.381
metaresearch head score (Gemma)0.492
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.381
Threshold uncertainty score0.764

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3810.492
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0120.014
Science and technology studies0.0030.002
Scholarly communication0.0070.007
Open science0.0040.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0050.003

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 head0.169
GPT teacher head0.438
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.

Study designNot applicable
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

Citations90
Published2021
Admission routes2
Has abstractyes

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