The SQUIRE Guidelines: A Scholarly Approach to Quality Improvement
Notice bibliographique
Résumé
Quality improvement (QI) work resonates with physicians and trainees, as it is immediately relevant to their work. Yet the academic medical community has largely avoided QI work, because it is perceived as time consuming, lacking scientific merit, and adversely impacting academic advancement. The Accreditation Council for Graduate Medical Education's Clinical Learning Environment Review program's focus on quality has prompted more physicians to do QI work, but often without the needed skill sets, which results in poorly conceived and ultimately unsuccessful improvement initiatives. Since this renders the work unpublishable, it further impedes progress in the field of health care improvement and widens the quality chasm.1 Academic physicians skilled in QI processes are crucial to the transformation of health care delivery. Along with trainees, they represent an untapped resource and are important players in addressing organizational quality problems.2The 2008 Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines sought to provide a structured approach to the reporting of, and lend legitimacy to, the scholarly dissemination of QI initiatives. In 2015, SQUIRE 2.0 was released as an update to these guidelines3 to minimize redundancies and offer a standardized framework for reporting and planning QI work.4The guidelines are broken down into 4 main sections: (1) Why did you start?; (2) What did you do?; (3) What did you find?; and (4) What does it mean? Each section is divided into subsections (18 total), which together delineate the key elements of a QI report. SQUIRE 2.0 more explicitly emphasizes the importance of articulating a rationale for proposed changes and describing the role of context, allowing authors and readers to determine the generalizability of this QI approach to other settings. Awareness and use of SQUIRE 2.0 guidelines can support academic physicians' and trainees' ability to complete rigorously conducted QI initiatives. The guidelines may facilitate greater engagement in and recognition for QI work in the academic environment.QI work may appear daunting. However, using the SQUIRE 2.0 guidelines as a starting point, interested faculty members or program directors can take simple steps to engage colleagues and trainees.Long-term efforts should be focused on building capacity for QI work in academic settings, and include investing in infrastructure at an institutional level and ensuring trained, incentivized faculty.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».