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Record W3189995523 · doi:10.1002/nop2.1027

An online survey to assess parents' preferences for learning about child health research

2021· article· en· W3189995523 on OpenAlexafffund
Lisa Knisley, Anne Le, Shannon D. Scott

Bibliographic record

VenueNursing Open · 2021
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsChildren's Hospital Research Institute of ManitobaUniversity of Alberta
FundersCanadian Institutes of Health ResearchKidscan Children's Cancer ResearchChildren's Hospital FoundationStollery Children’s Hospital Foundation
KeywordsReimbursementSurvey researchMedical educationPsychologyChild healthFamily medicineMedicineHealth careApplied psychologyPolitical science

Abstract

fetched live from OpenAlex

AIM: Ethical and logistical issues often exacerbate recruitment problems in child health studies. This study aims: (a) to provide new knowledge on how parents want to hear about child health research and (b) to inform the KidsCAN PERC iPCT initiative's re-examination of recruitment and retention strategies for pediatric emergency department research studies. DESIGN: We employed a cross-sectional, survey design. METHODS: An online survey was distributed to participants (i.e., parents) through partner organizations' advisory group mailing lists. Frequencies and measures of central tendency guided data analysis. RESULTS: Parents are interested in hearing about child health research opportunities, particularly during general practitioner, pediatrician or walk-in clinic visits. Most parents wanted updates on the research team, progress and results and support to participate, such as reimbursement of travel and childcare costs. Results can inform research teams in the planning of communications to effectively share research opportunities, progress and results with parents.

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)0.027
metaresearch head score (Gemma)0.056
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.056
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.933
GPT teacher head0.745
Teacher spread0.188 · 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 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

Citations4
Published2021
Admission routes2
Has abstractyes

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