Do plastic surgery division heads and program directors have the necessary tools to provide effective leadership?
Notice bibliographique
Résumé
Background Effective leadership is imperative in a changing health care landscape driven by increasing expectations in a setting of rising fiscal pressures. Because evidence suggests that leadership abilities are not simply innate but, rather, effective leadership can be learned, it is prudent for plastic surgeons to evaluate the training and challenges of their leaders because there may be opportunities for further growth and support. Objective To investigate the practice profiles, education/training, responsibilities and challenges of leaders within academic plastic surgery. Methods Following research ethics board approval, an anonymous online survey was sent to division heads and program directors from all university-affiliated plastic surgery divisions in Canada. Survey themes included demographics, education/training, job responsibilities and challenges. Results A response rate of 74% was achieved. The majority of respondents were male (94%), promoted to their current position at a mean age of 48 years, did not have a leadership-focused degree (88%), directly manage 30 people (14 staff, 16 faculty) and were not provided with a job description (65%). Respondents worked an average of 65 h per week, of which 18% was devoted to their leadership role, 59% clinically and the remainder on teaching and research. A discrepancy existed between time spent on their leadership role (18%) and related compensation (10%). Time management (47%) and managing conflict (24%) were described as the greatest leadership challenges by respondents. Conclusions Several gaps were identified among leaders in plastic surgery including predominance of male sex, limitations in formal leadership training and requisite skill set, as well as compensation and human resources management (emotional intelligence). Leadership and managerial skills are key core competencies, not only for trainees, but certainly for those in a position of leadership. The present study provides evidence that academic departments, universities and medical centres may benefit by re-evaluating how they train, promote and support their leaders in plastic surgery.
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 | 0,004 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».