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Record W4386765492 · doi:10.1002/wps.21133

Nurturing the next generation of clinician‐scientists in child and adolescent psychiatry: recommendations from a <scp>WPA</scp> Presidential Task Force

2023· article· en· W4386765492 on OpenAlexaff
Péter Szatmári, Christian Kieling, Andrea Raballo, Norbert Skokauskas, Bennett Leventhal

Bibliographic record

VenueWorld Psychiatry · 2023
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids FoundationCentre for Addiction and Mental Health
FundersMedical Research CouncilAcademy of Medical SciencesMQ: Transforming Mental Health
KeywordsWorkforceChild and adolescent psychiatryMedicinePopulationMental healthScarcityMedical educationPsychiatryResource (disambiguation)Political scienceEnvironmental health

Abstract

fetched live from OpenAlex

Clinician-scientists are members of the health care workforce who devote at least half of their time to research1. There is a concern throughout medicine that the number of clinician-scientists is woefully insufficient to meet the needs of the population. For example, the number of clinician-scientists in the US declined by 22% from 1983 to 20031. According to a 2012 report by the US National Institutes of Health2, clinician-scientists comprised only 1.5% of the total physician workforce. We were not able to find data on the proportion of clinician-scientists in child and adolescent psychiatry, but we believe that it is even lower than for other medical specialties. We are also not aware of any discussion of a human resource plan for child and adolescent psychiatry which includes an estimate of the number of clinician-scientists that the field needs and how this might be distributed across high- and low- or middle-income countries. Since the majority of the globe's children and youth live in low- or middle-income countries, the workforce needed to support mental health clinical innovation in these countries is a pressing human resource challenge. Research from other disciplines suggests that the lack of mentors and organized research training programs plays an essential role in determining the scarcity of clinician-scientists3. Key issues in child and adolescent psychiatry appear to be the lack of protected time during training to learn research methodology, read the literature, conduct pilot studies, and participate in mentors’ research. Research training in child and adolescent psychiatry is in a crisis. The solution depends on our determination to focus on the mental health of today's children and youth while simultaneously developing the resources necessary to support the mental health and well-being of children and youth of the future. We can only do this using innovative evidence-based treatments, generated by clinician-scientists working today and in the near future. There is evidence that clinician-scientist training programs are effective, at least in high-income countries, in medicine and surgery4 as well as in adult or general psychiatry5. There is only one report of a successful training program in child and adolescent psychiatry6. Ingredients of successful training programs include a strong synergy between a trainee's clinical and research interests7, an active support from department chairs and national policy makers, and availability of funds for the trainee to carry out initial, independent research separate from the mentor's scientific work. Our field is at a critical juncture. We fear that doing nothing will lead to the gradual “extinction” of clinician-scientists in child and adolescent psychiatry. By neglecting this priority, we will disadvantage the children who will need our services and our science in the decades to come. The time has come to address the mental health needs of future generations of children and youth who will be the beneficiaries of clinical innovation based on the work done today by clinician-scientists. The effectiveness of our clinical interventions in child and adolescent psychiatry can be improved only by supporting and nurturing the next generation of clinician-scientists in this field.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.681
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.115
GPT teacher head0.400
Teacher spread0.284 · 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 designNot applicable
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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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