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What makes a good psychiatrist? A survey of clinical tutors responsible for psychiatric training in the UK and Eire

2009· article· en· W1656795713 on OpenAlexaboutno aff
Dinesh Bhugra, K. Sivakumar, Gareth Holsgrove, Georgia Butler, Morven Leese

Bibliographic record

VenueWorld Psychiatry · 2009
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsnot available
Fundersnot available
KeywordsPsychiatryChild and adolescent psychiatryMedicineEmpathyService (business)Medical educationPsychology

Abstract

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The Royal College of Psychiatrists published Good Psychiatric Practice 1 in 2000, with a revised third edition in 2009. Modelled on Good Medical Practice 2 produced by the General Medical Council, core attributes for good psychiatric practice are listed as clinical competency, being a good communicator and listener, basic understanding of group dynamics, ability to work within a team, ability to be decisive and to appraise staff with a basic understanding of the principles of operational management, understanding the role and status of vulnerable patients, and bringing empathy and encouragement to patients and their carers, with critical awareness of emotional responses to clinical situations. Within the National Health Service (NHS), training for psychiatrists is organised in training schemes, with clinical tutors (approved by the Royal College of Psychiatrists) as individuals responsible for training, mentoring and supporting a number of trainees. Tutors have to support the application of a candidate to take the membership examination of the Royal College of Psychiatrists leading to the award of MRCPsych, which is a prerequisite for moving up to higher training leading to specialist status in one of six sub-specialities of psychiatry: adult general and community psychiatry, child and adolescent psychiatry, psychiatry of learning disability, forensic psychiatry, psychotherapy, and old age psychiatry. The role of proving training schemes has been taken over by an overarching Postgraduate Medical Education and Training Board (PMETB). However, PMETB has clearly laid out principles of training and assessment. Although the USA and Canada have had reports of competencies and evolving concepts for clinical practice and training 3,5, these concepts are being focused on in the UK only in recent times. Scheiber and Kramer 6 suggest that competencies can be measured along a sliding scale. Core competencies are the ones central to medical practice and are non-negotiable 4. Mikhael 7 has produced a list of competencies for speciality physicians. These are similar to the ones acknowledged in Good Psychiatric Practice and also include diagnostic capabilities, communication skills, collaborating, managing being a health advocate and a scholar. These competencies can be mapped on to different assessment methods. When trainees attain MRCPsych on the basis of examination, they have an obligation to meet standards of training and practice. Till recently, these standards were monitored by the Royal College of Psychiatrists, but since 30 September 2005 PMETB has taken on this responsibility. The clinical tutors are key individuals responsible for training and are in regular touch with trainees. We decided to approach them to obtain their views on the characteristics of a good psychiatrist. We used a postal survey, and all the tutors on the Royal College of Psychiatrists database were sent the questionnaire. In view of resource difficulties, no follow-up or reminders were arranged. The accompanying letter made it clear that there was no compulsion for response. The competencies from Good Psychiatric Practice were consolidated into ten competencies (see Table 1) and the respondents were asked to rate each competency as positive (by saying “yes”), negative (by saying “no”) or “did not know”. A simple tabular analysis was carried out. Of 163 clinical tutors who were approached, responses were received from 113 (69.3% response). The findings are illustrated in Table 1. There was an overwhelming agreement on the importance of overall clinical competency in diagnosis, investigations and management, being a good communicator and ability to make appropriate clinical decisions. The respondents were unable to say if ability to appraise staff, basic understanding of the principles of operational management, and having a basic understanding of group dynamics are desirable for this group of trainees. This brief survey highlights the typical and desirable characteristics of a good psychiatrist. Although it was a postal survey, the response rate was quite respectable. Although those who responded would be expected to be those with strong opinions, it seems unlikely that specific views of responders would be biased in any particular direction. It is not surprising that the most desirable characteristics are to do with clinical skills and competencies, which is what would be expected from psychiatrists. The surprising finding is that a majority of the respondents were unable to say if a knowledge of group dynamics is essential. This has been one of the requirements of the Royal College of Psychiatrists for training. Low emphasis on group dynamics indicates that there may be a shift away from general psychodynamic principles, as trainees used to be taught these. This may also be a reflection of a shortage of psychoanalytic therapists/trainers. The emphasis on operational management and staff appraisal is understandably low, as trainees will not be expected to participate in these activities although they have started undergoing appraisal. It is also possible that the role of these activities is clearer to organisations and institutions such as the Royal College of Psychiatrists and hospitals but not to trainers or trainees. With recent changes in training and assessment in the UK, further surveys of this kind are indicated to understand the trainers’ views, and should be preferably extended also to trainees. The authors would like to thank all the participants for their responses.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.140
GPT teacher head0.489
Teacher spread0.348 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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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Citations11
Published2009
Admission routes1
Has abstractyes

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