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Record W4205824638 · doi:10.3148/cjdpr-2021-036

Patient-Reported Outcome and Experience Measures Administered by Dietitians in the Outpatient Setting: Systematic Review

2022· article· en· W4205824638 on OpenAlexvenueno aff
Kelly Lambert, Jordan Stanford

Bibliographic record

VenueCanadian Journal of Dietetic Practice and Research · 2022
Typearticle
Languageen
FieldHealth Professions
TopicDietetics, Nutrition, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePatient experienceOutpatient clinicPatient-reported outcomeMEDLINEHealth careQuality of life (healthcare)Family medicinePhysical therapyNursingInternal medicine

Abstract

fetched live from OpenAlex

Understanding how patients perceive their health and the experience with the dietitian is fundamental to providing patient-centred care. The types of patient reported measures (PRMs) used by outpatient dietitians is unclear. Guidance about use of PRMs for dietitians is also lacking. The aim of this systematic review was to synthesise evidence regarding the use of PRMs by dietitians in the outpatient setting and evaluate the methodological quality of studies evaluating the psychometric properties of PRMs. Eight databases were searched systematically for studies of dietitians working in the outpatient setting and administering a PRM. Forty-four studies were evaluated and described 58 different PRMs. These included direct nutrition related (n = 12 studies), clinical (n = 21 studies), and health-related quality of life PRMs (n = 24 studies); 1 study documented use of a patient-reported experience measure. A large range of PRMs are used by outpatient dietitians. Of the most common PRMs, the majority are administered in similar populations to the original validation study. Dietitians should use a combination of 3 PRMs: a generic health-related quality of life tool, an experience measure, and at least 1 clinical or direct nutrition-related measure. This will enable dietitians to fully capture the impact of their care on patients.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.226
GPT teacher head0.494
Teacher spread0.268 · 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 teacher head, not a consensus.

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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Dietetic Practice and ResearchSame topicDietetics, Nutrition, and EducationFrench-language works237,207