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Record 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 on OpenAlexaff
Britt Borregaard, Angela Massouh, Jeroen Hendriks, Ian Jones, Geraldine Lee, Panagiota Manthou, Catherine Sheldrick Ross, Suzanne Fredericks, Julie Sanders

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2021
Typeletter
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMedicineSupervisorMedical educationManagement

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.990
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.055
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0080.003
Scholarly communication0.0060.006
Open science0.0020.004
Research integrity0.0190.024
Insufficient payload (model declined to judge)0.0260.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.153
GPT teacher head0.343
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations4
Published2021
Admission routes1
Has abstractyes

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