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Record W2101366295 · doi:10.1111/bju.13338

Fellowships: more not less to improve employability and patient outcomes

2015· letter· en· W2101366295 on OpenAlexaboutno aff
Nathan Lawrentschuk

Bibliographic record

VenueBritish Journal of Urology · 2015
Typeletter
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsnot available
Fundersnot available
KeywordsEmployabilityDilemmaMedicineLearning curveMedical educationMargin (machine learning)PsychologyManagementPedagogy

Abstract

fetched live from OpenAlex

The noose on consultant urological jobs in Australia and New Zealand has largely tightened to the point where trainees are now faced with dilemmas – do I stay locally in case a job turns up or do I go elsewhere, particularly overseas to improve chances of employment but risk missing a local job? The logical response being if you go on a good quality fellowship then you gain greater experience, better skilled, more employable and hence circumvent the first dilemma. But it is not that easy: emotion, relationships, family and costs are among the many competing interests along with the intangible of ‘uncertainty’. So how should trainees proceed? What value should we place on fellowships today? Well, lets look at the recent facts. Importantly, do fellowships improve patient outcomes? Johnston et al. 1 in a recent systematic review and meta-analysis found that fellowship training appears to have a positive impact on patient outcomes. This worked for the fellowship-trained surgeons but also for the centres having fellowships. Surgeons without fellowship training converted more laparoscopic operations to open surgery than those with fellowship training. Further, the mortality rate for patients in centres with an affiliated fellowship program was lower than that for centres without, as was the rate of complications. Also we are all well aware of the surgical learning curve. There is evidence to suggest that fellowship-trained surgeons have a different learning curve to those of surgeons who are not fellowship trained. It has been suggested that with radical prostatectomy, positive surgical margin levels are similar between fellowship- and non-fellowship-trained surgeons at the start of their learning curve but that the non-fellowship-trained surgeons fail to improve with experience past a defined point 2. Closer to home, the Victorian Radical Prostatectomy Registry has just accumulated data demonstrating that supervisor volume affects oncological outcomes of trainees performing open radical prostatectomy 3. Fellowship-trained surgeons appeared to have better outcomes and were better teachers. The next question arises is which fellowship to do? Certainly an accredited fellowship with a learned society will hold sway (e.g. The Society of Urologic Oncology, Endourological Society, Society for Urodynamics and Female Urology). This is because they are structured, inspected and uphold standards as expected by the sub-specialty societies. They are typically longer (minimum two years) and take place at larger, generally high-volume and well-respected centres. However, there are still good unaffiliated fellowships that provide valuable skills. These need to be investigated and understood to maximize the experience. It is also important to see what skills you aspire to obtain and how that fits into a region or hospital where you may wish to return or end up in the future. Throughout all levels of training, moving oneself in a relationship or a family interstate or overseas is a challenge: juggling of partner careers, children, negotiating new health systems, transport, cultures are all challenges that must be overcome. Fellowships may add more such stressors and current fellowships are fairly inflexible. Can the urological community do better? Shared posts, flexible hours and alike are proposed but have only started to be considered and as such remain barriers to many. Perhaps USANZ could push for flexible combined local clinical and research posts on its own or with assistance from organisations such as our affiliated research foundation and/or other sources? Finally the training of fellows has been alluded to as a good outcome for patients – but is it good for trainees? A study aptly titled “Fellow or foe: the impact of fellowship training programs on the education of Canadian urology residents” attempted to explore attitudes of trainees 4. The jury was out. They cautioned that program directors must clearly define the role of the fellow and outline the limits of surgical practice, establish clear and consistent guidelines outlining responsibilities (operative, clinical and on-call), and open lines of communication to ensure that all opinions are recognized and addressed. Also, they stressed that fellows should have proficient technical skills, clinical knowledge, teaching ability and work ethic to be selected to ensure that they focus on ‘specialized’ training (i.e. so that fellowships are de facto training schemes). We await some local data currently being gathered along the same lines. In summary, back to the dilemma. It appears that fellowships may lead to better outcomes with our patients and we should where feasible encourage our trainees to continue to seek and complete fellowships as a way to uphold and extend skills. So doing a fellowship is likely not detrimental to patients or your career but must be balanced against any personal and other costs. The systems have yet to move with the times and our Society should be leading the charge. Lastly, we should consider that taking on fellows in appropriate posts as there may be benefits, but we must be aware that it may diminish the training experience of our local trainees and this needs to be carefully monitored. The author completed an accredited 2-year Society of Urologic Oncology Fellowship at the Princess Margaret Hospital, University of Toronto, Canada.

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)0.007
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.045
GPT teacher head0.307
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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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Citations0
Published2015
Admission routes1
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