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Record W3013318898 · doi:10.2196/14997

Improving Understanding of Participation and Attrition Phenomena in European Cohort Studies: Protocol for a Multi-Situated Qualitative Study

2020· article· en· W3013318898 on OpenAlexvenueno aff
Sandra Marques, Julia Nadine Doetsch, Anne Brødsgaard, Marina Cuttini, Elizabeth S. Draper, Eero Kajantie, Jo Lebeer, Sylvia van der Pal, Pernille Pedersen

Bibliographic record

VenueJMIR Research Protocols · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsnot available
FundersNovo Nordisk Fonden
KeywordsThematic analysisContext (archaeology)CohortTriangulationQualitative researchFocus groupAttritionCohort studyPsychologyReflexivityQualitative propertyDevelopmental psychologyMedicineApplied psychologyMedical educationSociologyComputer scienceGeographySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: Cohort studies represent a strong methodology for increasing one's understanding of human life-course development and etiological mechanisms. Retention of participants, especially during long follow-up periods, is, however, a major challenge. A better understanding of the motives for participation and attrition in cohort studies in diverse sociogeographic and cultural settings is needed, as this information is most useful in developing effective retention strategies. OBJECTIVE: This study aims to improve our understanding of participation and attrition phenomena in a European cohort study of very preterm/very-low-birth-weight (VPT/VLBW) infants from various sociogeographic and cultural settings to better understand variability and ultimately contribute to developing novel and more "in-context" strategies to improve retention. METHODS: This study uses a triangulation of multisituated methods to collect data on various cohorts in the Research on European Children and Adults Born Preterm (RECAP) network, which include focus group discussions, individual semidriven interviews, and a collaborative, reflexive visual methodology (participant-generated VideoStories) with relevant key actors involved with these cohort studies such as adult participants, parents (caregivers), cohort staff, health care professionals, and academic researchers. The methodological strategy aims to provide a shared flexible framework of various qualitatively driven methods to collect data on VPT/VLBW adult and child cohorts, from which research partners may choose and combine those most pertinent to apply in their own specific contexts. Data from all sources and sites will be submitted to a triangulation of phenomenological thematic analysis with discourse analysis. RESULTS: As of January 2020, in this study, we enrolled 92 participants variously involved with child and adult RECAP partnering cohorts from six countries. Multisite enrollment and data collection are expected to be completed in all seven study settings by June 2020. Findings will be reported in future publications. CONCLUSIONS: Qualitative research methods are a useful complement for enriching and illuminating quantitative results. We expect that opting for a multisituated study approach addressing the interplay of the lived experience of individuals in both researcher and researched stances of particular cohort study settings will contribute to filling some gaps in the understanding of participation variability and effectiveness of different implemented strategies in context. Moreover, health research subjects have traditionally been positioned as passive objects of study rather than active participants, even though they have the greatest stake in improving health care policies and practices. Including collaborative methods allows us to counteract the "top-down" model by handing over some research control to the very people who are providing the data on which research findings will be based while also acknowledging the value of their involvement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14997.

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)0.157
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.843
Threshold uncertainty score0.831

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1570.105
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0050.005
Science and technology studies0.0090.006
Scholarly communication0.0050.005
Open science0.0060.007
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0570.013

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 head0.769
GPT teacher head0.639
Teacher spread0.130 · 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 designQualitative
DomainMethods
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

Citations12
Published2020
Admission routes1
Has abstractyes

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