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Record W2553147471 · doi:10.1186/s12913-016-1904-6

Methods used to address fidelity of receipt in health intervention research: a citation analysis and systematic review

2016· review· en· W2553147471 on OpenAlexaff
Lorna Rixon, Justine Baron, Nadine McGale, Fabiana Lorencatto, Jill Francis, Anna Davies

Bibliographic record

VenueBMC Health Services Research · 2016
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsOttawa Hospital
FundersCity, University of London
KeywordsReceiptFidelityCitationHealth informaticsPsychological interventionMedicineData extractionSystematic reviewIntervention (counseling)BiostatisticsNursing researchPublic healthMEDLINEComputer scienceNursingWorld Wide WebPolitical science

Abstract

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BACKGROUND: The American Behaviour Change Consortium (BCC) framework acknowledges patients as active participants and supports the need to investigate the fidelity with which they receive interventions, i.e. receipt. According to this framework, addressing receipt consists in using strategies to assess or enhance participants' understanding and/or performance of intervention skills. This systematic review aims to establish the frequency with which receipt is addressed as defined in the BCC framework in health research, and to describe the methods used in papers informed by the BCC framework and in the wider literature. METHODS: A forward citation search on papers presenting the BCC framework was performed to determine the frequency with which receipt as defined in this framework was addressed. A second electronic database search, including search terms pertaining to fidelity, receipt, health and process evaluations was performed to identify papers reporting on receipt in the wider literature and irrespective of the framework used. These results were combined with forward citation search results to review methods to assess receipt. Eligibility criteria and data extraction forms were developed and applied to papers. Results are described in a narrative synthesis. RESULTS: 19.6% of 33 studies identified from the forward citation search to report on fidelity were found to address receipt. In 60.6% of these, receipt was assessed in relation to understanding and in 42.4% in relation to performance of skill. Strategies to enhance these were present in 12.1% and 21.1% of studies, respectively. Fifty-five studies were included in the review of the wider literature. Several frameworks and operationalisations of receipt were reported, but the latter were not always consistent with the guiding framework. Receipt was most frequently operationalised in relation to intervention content (16.4%), satisfaction (14.5%), engagement (14.5%), and attendance (14.5%). The majority of studies (90.0%) included subjective assessments of receipt. These relied on quantitative (76.0%) rather than qualitative (42.0%) methods and studies collected data on intervention recipients (50.0%), intervention deliverers (28.0%), or both (22.0%). Few studies (26.0%) reported on the reliability or validity of methods used. CONCLUSIONS: Receipt is infrequently addressed in health research and improvements to methods of assessment and reporting are required.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchBibliometrics
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptMetaresearchBibliometrics
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.314
metaresearch head score (Gemma)0.654
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.833
Threshold uncertainty score0.846

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3140.654
Meta-epidemiology (narrow)0.0060.005
Meta-epidemiology (broad)0.0240.026
Bibliometrics0.1670.126
Science and technology studies0.0050.006
Scholarly communication0.0150.015
Open science0.0080.014
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0180.002

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 head0.877
GPT teacher head0.814
Teacher spread0.063 · 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

Labeled directly by 2 models reading the full record.

Study designSystematic review
DomainMethods
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

Citations120
Published2016
Admission routes1
Has abstractyes

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