Normes de présentation de recherche utilisant les protocoles à cas unique en interventions comportementales (SCRIBE-2016)
Bibliographic record
Abstract
Nous avons élaboré des normes de présentation (sorte de lignes directrices) servant de guide pratique pour la rédaction d’un article à publier dans une revue scientifique, utilisantun design de recherche particulier : le protocole expérimental à cas unique. Le présent article décrit la procédure utilisée afin d’élaborer ces normes : « single-case reporting guideline in behavioural interventions » (SCRIBE-2016). À l’issue de deux enquêtes en ligne et d’une conférence de consensus réunissant des experts durant deux jours, une liste de conformité (check-list) SCRIBE-2016 comprenant 26 items que les auteurs devraient considérer lorsqu’ils rédigent des travaux utilisant les protocoles à cas unique a été établie. Cet article complète ainsi l’article intitulé « SCRIBE-2016 explication and elaboration » (Tate et al., 2016) qui détaille et explique chaque item de la liste tout en fournissant, en guise de modèle, des illustrations pratiques tirés de travaux exemplaires de la littérature. Ces deux ressources aideront les auteurs à rédiger avec clarté, exhaustivité, exactitude et transparence des articles de recherche portant sur des cas uniques. Elles fourniront également aux experts des revues ainsi qu’à leurs rédacteurs en chef une liste de vérification pratique qui leur servira de grille de lecture critique de ces articles. Nous recommandons de fait que le SCRIBE-2016 soit utilisé aussi bien par les auteurs qui envisagent de publier des manuscrits rendant compte d’une recherche à cas unique, que par les experts et éditeurs de revues qui les évaluent. We developed a reporting guideline to provide authors with guidance about what should be reported when writing a paper for publication in a scientific journal using a particular type of research design: the single-case experimental design. This report describes the methods used to develop the single-case reporting guideline in behavioural interventions (SCRIBE) 2016. As a result of 2 online surveys and a 2-day meeting of experts, the SCRIBE 2016 checklist was developed, which is a set of 26 items that authors need to address when writing about single-case research. This article complements the more detailed SCRIBE 2016 explanation and elaboration article (Tate et al., 2016) that provides a rationale for each of the items and examples of adequate reporting from the literature. Both these resources will assist authors to prepare reports of single-case research with clarity, completeness, accuracy, and transparency. They will also provide journal reviewers and editors with a practical checklist against which such reports may be critically evaluated. We recommend that the SCRIBE 2016 is used by authors preparing manuscripts describing single-case research for publication, as well as journal reviewers and editors who are evaluating such manuscripts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".