Nurturing the next generation of clinician‐scientists in child and adolescent psychiatry: recommendations from a <scp>WPA</scp> Presidential Task Force
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.126 | 0.137 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.014 | 0.024 |
| Research integrity | 0.052 | 0.044 |
| Insufficient payload (model declined to judge) | 0.022 | 0.017 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".