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Record W2914839618 · doi:10.1136/bmjopen-2018-024725

Identifying priorities for cancer caregiver interventions: protocol for a three-round modified Delphi study

2019· article· en· W2914839618 on OpenAlexaff
Sarah‐May Blaschke, Sylvie Lambert, Patricia M. Livingston, Sanchia Aranda, Anna Boltong, Penelope Schofield, Suzanne K. Chambers, Meinir Krishnasamy, Anna Ugalde

Bibliographic record

VenueBMJ Open · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsOccupational Cancer Research CentreMcGill University
FundersVictorian Cancer Agency
KeywordsMedicineProtocol (science)Delphi methodPsychological interventionFamily medicineDelphiMedical educationNursingAlternative medicinePathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Cancer is often considered a chronic disease, and most people with cancer have a caregiver, often a family member or friend who provides a significant amount of care during the illness trajectory. Caregivers are frequently in need of support, and a range of interventions have been trialled to improve outcomes. Consensus for optimal ways to support caregivers is not known. The aim of this protocol paper is to describe procedures for a modified Delphi study to explore expert consensus about important factors when developing caregiver interventions. METHODS AND ANALYSIS: Online modified Delphi methodology will be used to establish consensus for important caregiver intervention factors incorporating the Patient problem, Intervention, Comparison and Outcome framework. Round 1 will comprise a free-text questionnaire and invite the panel to contribute factors they deem important in the development and evaluation of caregiver interventions. Round 2 is designed to determine preliminary consensus of the importance of factors generated in round 1. The panel will be asked to rate each factor using a 4-point Likert-type scale. The option for panellists to state reasoning for their rating will be provided. Descriptive statistics (median scores and IQR) will be calculated to determine each item's relative importance. Levels of consensus will be assessed based on a predefined consensus rating matrix. In round 3, factors will be recirculated including aggregate group responses (statistics and comment summaries) and panellists' own round 2 scores. Panellists will be invited to reconsider their judgements and resubmit ratings using the same rating system as in round 2. This will result in priority lists based on the panel's total rating scores. ETHICS AND DISSEMINATION: Ethics for this study has been gained from the Deakin University Human Ethics Advisory Group. It is anticipated that the results will be published in peer-reviewed journals and presented in a variety of forums.

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)0.095
metaresearch head score (Gemma)0.074
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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.095
Threshold uncertainty score0.501

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0950.074
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0040.003
Science and technology studies0.0060.004
Scholarly communication0.0050.004
Open science0.0050.006
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0660.013

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 head0.357
GPT teacher head0.541
Teacher spread0.184 · 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
GenreProtocol

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

Citations14
Published2019
Admission routes1
Has abstractyes

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