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Ecological content validation of the Information Assessment Method for parents (IAM-parent): A mixed methods study

2017· article· en· W2762894781 on OpenAlexafffund
Mathieu Bujold, Reem El Sherif, Paula Louise Bush, Janique Johnson‐Lafleur, Geneviève Doray, Pierre Pluye

Bibliographic record

VenueEvaluation and Program Planning · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsCentre de Santé et de Services Sociaux de la MontagneLucie and André Chagnon FoundationMcGill University
FundersFonds de Recherche du Québec - SantéMcGill University
KeywordsRepresentativeness heuristicQualitative propertyPsychologyMultimethodologyRelevance (law)Content analysisQualitative researchSample (material)Applied psychologyComputer scienceSocial psychologyChemistryMachine learningSociologyChromatographySocial sciencePolitical science

Abstract

fetched live from OpenAlex

This mixed methods study content validated the Information Assessment Method for parents (IAM-parent) that allows users to systematically rate and comment on online parenting information. Quantitative data and results: 22,407 IAM ratings were collected; of the initial 32 items, descriptive statistics showed that 10 had low relevance. Qualitative data and results: IAM-based comments were collected, and 20 IAM users were interviewed (maximum variation sample); the qualitative data analysis assessed the representativeness of IAM items, and identified items with problematic wording. Researchers, the program director, and Web editors integrated quantitative and qualitative results, which led to a shorter and clearer IAM-parent.

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)0.259
metaresearch head score (Gemma)0.341
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.914

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2590.341
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.244
GPT teacher head0.551
Teacher spread0.307 · 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 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

Citations25
Published2017
Admission routes2
Has abstractyes

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