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Enregistrement W4417002431 · doi:10.1182/blood-2025-6426

Evaluation of the American society of hematology quality improvement training institute: Impact on learner quality improvement knowledge, confidence, and satisfaction

2025· article· en· W4417002431 sur OpenAlexaff
Michael Keng, Menaka Pai, Tanya Thomas, Ming Y. Lim, Rachael F. Grace, MJ Duckwitz, Emily Cahill, Sarah Paliani

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueRadiology practices and education
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésLikert scaleQuality managementScale (ratio)Multidisciplinary approachBaseline (sea)Program evaluationQuality (philosophy)Qualitative property

Résumé

récupéré en direct d'OpenAlex

Abstract Background: The American Society of Hematology (ASH) Quality Improvement Training Institute (QITI) launched in March 2024 to build capacity among hematologists and hematology professionals to lead quality improvement (QI) efforts QITI comprised 7 total workshops (3 in-person and 4 virtual), monthly individualized coaching, and the implementation of two QI projects per team, engaging 26 learners across multidisciplinary teams representing 5 institutions. We describe the program's impact on learners' self-reported QI knowledge, confidence, and satisfaction over the 16-month training period. Methods: A prospective mixed-methods program evaluation was conducted using an abridged version of the Beliefs, Attitudes, and Skills in Confidence in QI (BASIC-QI) scale at baseline (Mar 2024), mid-program (Jan 2025), and program end (Jun 2025). BASIC-QI includes two subscales: a 9-domain QI knowledge scale (7-point Likert Strongly Disagree to Strongly Agree) and a 12-domain QI confidence scale (4-point Likert Not at all confident to Extremely confident). Program satisfaction was assessed at program end via web-based survey, examining learner overall satisfaction with the program, coach effectiveness, and learner beliefs about sustainment of QI project success and program impact on career. Analyses compared mean scores over time and explored differences by participation modality (in-person vs. virtual). Qualitative analysis of the learning objectives and overall experience was conducted using open-ended responses collected from program assessments. Results: Among learners with complete data on the QI knowledge assessments (69% response), average QI knowledge scores improved from 3.98 at baseline to 6.27 at program end (p<.001), while QI confidence improved from 1.86 to 3.13 (p<.001). Improvements were observed in both in-person and virtual cohorts, though gains were slightly higher among in-person learners (knowledge improvement: 2.42 vs. 2.04 [p<.001]; confidence improvement: 1.39 vs. 1.03 [p<.01]). The majority of knowledge gains (83%) and confidence gains (65%) occurred between baseline and mid-program assessment, corresponding with the program’s focus on didactic learning and intensive coaching in the first phase of the program. Continued confidence growth during the latter half of the program aligned with increased learner autonomy and expanded scope of institutional QI work. Learners reported growth in QI knowledge particularly in the use of tools like the PDSA cycle and expressed enthusiasm for applying these skills within their institutions. They valued the program’s structure, coaching, and sense of community, while also recommending clearer timelines, expectations, and more time for project implementation in future cohorts. Results from the program satisfaction survey (77% response) indicate a high level of satisfaction and strong perceived impact on QI skills and career development. Respondents agreed or strongly agreed that the program met their expectations, were satisfied overall, and would recommend QITI to colleagues, with satisfaction notably higher among in-person learners. For example, 77% of in-person learners strongly agreed they would recommend QITI, compared to only 14% of virtual learners (with the remainder agreeing). Respondents reported high satisfaction with their coaches, with 75% strongly agreeing that coaches were knowledgeable, supportive, and provided constructive feedback. Respondents overwhelmingly agreed that QITI improved their competence and confidence in conducting QI, with 90% agreeing or strongly agreeing that QITI helped advance their careers in QI.Conclusions: ASH QITI significantly improved learners’ self-reported QI knowledge and confidence. Early structured learning drove initial knowledge acquisition, while continued project engagement supported sustained growth in confidence. Findings suggest QITI effectively enhanced learners’ QI skills and professional development, with greater perceived impact among in-person attendees. These findings support the value of structured, longitudinal QI training and coaching in hematology, and emphasize the potential benefit of in-person participation for maximizing engagement and program effect. Longer term evaluation is needed to validate gains in QI knowledge and confidence, assess QI project sustainability and development of future QI projects, and understand overall impact on alumni long-term career trajectory.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,025
score de la tête « metaresearch » (Gemma)0,028
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,025
Score d'incertitude au seuil0,131

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0250,028
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,099
Tête enseignante GPT0,451
Écart entre enseignants0,352 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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