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Innovative Recruitment Strategies Increase Diversity of Participation in the Fox Insight Longitudinal Cohort (S16.009)

2019· article· en· W3006453706 on OpenAlexaff
Roseanne D. Dobkin, Ninad Amondikar, Chelsea Caspell‐Garcia, Janel Barnes, Lauren Bataille, Lana M. Chahine, Andrea Katz, Catherine Kopil, Connie Marras, Amanda Melnick, Traci Schwieger, Bernadette Siddiqi, Luba Smolensky, David G. Standaert, Caroline M. Tanner

Bibliographic record

VenueNeurology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsDiversity (politics)CohortLongitudinal studyGerontologyCohort studyMedicineSociologyInternal medicineAnthropologyPathology

Abstract

fetched live from OpenAlex

In order to truly advance Parkinson’s disease (PD) research and clinical care, innovative recruitment strategies are required to accurately capture patient reported outcomes from diverse segments of the PD community. The objectives of this project were to examine whether specific digital marketing campaigns were associated with increased targeted enrollment in the Fox Insight Study (early and late stage disease, underrepresented geographic areas), compared to a baseline period with no special promotions, and to further describe the clinical and demographic characteristics of the PD samples recruited via different methods.

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)0.010
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.201
GPT teacher head0.429
Teacher spread0.228 · 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 designObservational
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

Citations2
Published2019
Admission routes1
Has abstractyes

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