Understanding the training, mentorship, and professional development priorities of early career embedded researchers
Notice bibliographique
Résumé
INTRODUCTION: Health systems are constantly evolving in response to existing and emerging health challenges and are increasingly adopting the Quintuple Aim to guide transformation and improvement efforts. Addressing health challenges and achieving the Quintuple Aim (enhancing patient experience, improving healthcare provider experience, promoting population health, optimising the value of healthcare services, and advancing health equity) may be enhanced with the use of a Learning Health Systems approach that fosters the real-time use of data and evidence to inform improvement efforts and harnesses embedded researchers to co-produce timely, relevant evidence to address priorities. Training programs have emerged to build embedded research capacity within health system organisations and have focused predominantly on the postdoctoral career stage, with little attention paid to the early career researcher (ECR) stage. The objective of this study was to understand ECR training and mentorship needs in the embedded research context to inform the creation new or adaptation of existing programs to build embedded ECR capacity. METHODS: This study used a qualitative approach to garner insight from embedded and applied scholars and health systems leaders in Canada from various professional backgrounds and at various career stages using a combination of focus group discussions, key informant interviews, and an online survey. Thematic content analysis was used to examine the responses of study participants within the interview themes. RESULTS: Twenty-six (26) participants were included in the study. Results were organised according to four key themes: (1) key competencies and skills needed by embedded ECRs; (2) additional training and capacity development needs; (3) training delivery approaches; and (4) enablers and challenges faced by embedded ECRs. Results highlight the importance of supporting ECRs to develop their leadership and organisational management capabilities; their knowledge of and ability to use research approaches that are well-suited to real-world, complex, evolving environments; and their opportunities to learn with and from each other and mentors. Results underscore the perceived importance of context, including being embedded in a supportive environment that values research and evidence and of academic incentives that recognise and value real-world research impact. The challenges of responding to shifting organisational and system priorities were identified. Additional insights from health systems leaders were also highlighted. CONCLUSION: This study identified the multifaceted needs of embedded ECRs and the challenges they face within healthcare systems. Designing new programs or tailoring existing ones to address these needs would build their capacity, foster career progression, and ensure their impact as leaders of evidence-informed health system improvement which is crucial for achieving the Quintuple Aim.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,061 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,009 |
| Communication savante | 0,013 | 0,009 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».