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Enregistrement W3204942301 · doi:10.1093/eurjcn/zvab081

The X-factors of PhD supervision: ACNAP top 10 tips on choosing a PhD supervisor

2021· letter· en· W3204942301 sur OpenAlexaff
Britt Borregaard, Angela Massouh, Jeroen Hendriks, Ian Jones, Geraldine Lee, Panagiota Manthou, Catherine Sheldrick Ross, Suzanne Fredericks, Julie Sanders

Notice bibliographique

RevueEuropean Journal of Cardiovascular Nursing · 2021
Typeletter
Langueen
DomaineMedicine
ThématiqueHealth and Medical Research Impacts
Établissements canadiensToronto Metropolitan University
Organismes subventionnairesnon disponible
Mots-clésMedicineSupervisorMedical educationManagement

Résumé

récupéré en direct d'OpenAlex

Research culture and activity improves patient outcomes,1 benefits the quality, safety, and efficiency of patient care,2 and influences health policy—which must include that undertaken by nurses and other health professionals.3 Doctoral programmes exist to prepare candidates to become committed, skilful, independent researchers who will lead future research to improve patient outcomes and experience. Although nursing doctoral programmes have been around since the early 1930s, there is a shortage of doctorally prepared nurses,4 which continues to be a barrier to advancing both care delivery and the profession.5 Thus, there is international recognition that increasing nursing research capacity and doctoral education is needed, including in low- to middle-income countries6 and that the quality of doctoral education is paramount.4 This is especially true in cardiovascular disease (CVD). Despite a European population of over 748 million, it is estimated that the number of doctorally prepared CVD nurses is very low (approximately 200–300).7 Since more people than ever before are living with CVD, with significant increases in both disability-adjusted life years and years lived with disability,8 the Association of Cardiovascular Nursing and Allied Professions (ACNAP) mission ‘to support nurses and allied professionals throughout Europe to deliver the best possible care to patients with CVD and their families’ has never been more important. Thus, efforts to build nursing and allied professional research capacity in CVD is essential. A key factor in doctoral education is finding an appropriate academic supervisor. The PhD student–supervisor relationship is complex, but there is no agreed consensus on what constitutes excellence in PhD supervision and the quality of doctoral training in nursing is noted to be variable.4 However, since the student is significantly dependent on the supervisor for successful and timely PhD completion, and the foundations of their future post-doctoral career, effective research supervision is of utmost importance. Typically, many doctoral supervisors rely on traditional methods which include face-to-face meetings with a lack of opportunities between supervisions for communication.9 However, just as with clinical care, a more person-centred approach to PhD supervision is now recommended.10 Due to the importance of doctoral supervision coupled with the international need to increase nursing and allied professional research capacity in CVD, the ACNAP Science Committee sought to define a ‘top 10’ criteria of supporting potential PhD candidates in choosing their primary academic PhD supervisor, based on their vast collective experience, and supported by the evidence. Conversely, since many PhD supervisors report feeling underprepared for this role,9 this ‘top 10’ may be useful for prospective PhD supervisors in preparing themselves to become successful PhD supervisors. Our top 10 tips include consideration of experience, flexibility and openness, creative thinking, approachability, commitment and availability, support and mentorship, ethics and integrity, organization, working style, and ‘red flags’ (Figure 1). These are not all mutually exclusive, and as we are all different, varying emphasis will be placed on each depending on personal preference, motivations, and circumstance. Furthermore, while the majority of the literature in this area relates to nursing, the applicability to allied professionals, and across specialities beyond CVD, is likely to be universal—these should all be characteristics of any good PhD supervisor irrespective of profession or speciality. The ACNAP Science Committee summary of the top 10 tips for choosing a PhD supervisor. The ACNAP Science Committee summary of the top 10 tips for choosing a PhD supervisor. A key point is that the primary supervisor has experience. This includes a national and international track record of research in the relevant area, previous successful PhD supervision, and expertise on actively developing nursing and allied professional academic and clinical academic research capacity and careers. We surmise that the extent of experience potentially, but not always, underpins many of the other top 10 tips, as their supervision is then less likely to be based on repeating the same supervisory style they received.10 However, being a good PhD supervisor is much more than just being clinically and academically experienced. Having a flexible and open approach involving active communication and engagement to encourage transformational learning,11 alongside providing a safe, positive research environment that promotes creative thinking, allowing PhD students to grow with the freedom to challenge,12 is essential. Similarly, the student–supervisor relationship should be based on mutual respect so choose a PhD supervisor that demonstrates ethics and integrity. Ethics in research supervision is more than just ‘official approvals’ but includes characteristics such as caring, dignity, responsibility, and virtue13 which are highly regarded characteristics in any discipline or speciality. Consideration is also needed for more practical working factors, such as working style, organization, and commitment and availability. These areas often cause angst for PhD students, who have potentially unrealistic expectations regarding feedback, supervisor availability, and level of support they will receive.9 Therefore, it is important to explore these issues with any potential supervisor so that expectations on both sides in terms of roles, responsibilities, and ways of working are discussed and addressed. The sign of an excellent PhD supervisor is their ability to be flexible to the needs of the student and establishing mutually agreeable ways of working, perhaps through a contractual framework, can be helpful. In terms of commitment and availability, it is also important to explore the potential for your supervisor’s commitment and availability for providing support and guidance beyond the PhD. It is worth noting that many nurses have anxieties about the post-doctoral clinical academic careers,14 and this coupled with limited post-doctoral career opportunities and positions available6,15 means that continued mentorship and guidance to navigate a successful clinical academic career is highly valuable. So far, we have focused more on the scholarly activities and qualities we believe constitutes a good PhD supervisor. However, PhD students also need and expect emotional support. Since depression and anxiety are common in doctoral students16 a PhD supervisor with emotional availability and approachability is very important. Supervisors should exhibit emotional intelligence and the ability to provide a psychologically safe environment.17 This is further emphasized through support and mentorship, where alongside practical and academic expert supervision, caring and supportive attributes11 should be displayed. Ideally, as indicated previously, this should also extend beyond the PhD. Finally, it is important to consider any ‘red flags’. These can include poor reflections or completion rates from current or past PhD students, a lack of publications from the supervisors themselves or their students, a poor workplace culture with a high turnover of staff and/or an expectation that PhD students should always be available, which are just some examples. It is advisable to meet with a prospective supervisor and meet their team to establish if you think, in combination with all of the other top 10 tips, whether the supervisor–PhD student relationship on offer is a ‘good fit’ for you. These top 10 tips have explicitly focused on the supervisor attributes that the prospective PhD candidate should explore prior to embarking on their PhD to maximize opportunities for success both for the PhD and future clinical academic career. These include consideration of experience, flexibility and openness, creative thinking, approachability, commitment and availability, support and mentorship, ethics and integrity, organization, working style, and other red flags. However, we have also highlighted that the student–supervisor relationship is complex, and the responsibility for this work does not rest purely with the supervisor. The key is finding the right ‘match’ of all of these ‘X-Factors’ and when you find it you will know. Conflict of interest: none declared. The opinions expressed in this article are not necessarily those of the Editors of the European Heart Journal or of the European Society of Cardiology.

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)0,010
score de la tête « metaresearch » (Gemma)0,055
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,990
Score d'incertitude au seuil0,087

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,055
Méta-épidémiologie (sens strict)0,0000,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0080,003
Communication savante0,0060,006
Science ouverte0,0020,004
Intégrité de la recherche0,0190,024
Charge utile insuffisante (le modèle a refusé de juger)0,0260,009

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 Opus0,153
Tête enseignante GPT0,343
Écart entre enseignants0,191 · 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.

Devis d'étudeSans objet
DomaineIncitatifs
GenreCommentaire

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

En bref

Citations4
Publié2021
Routes d'admission1
Résumé présentoui

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