Barriers to and facilitators of success for early and <scp>Mid‐Career</scp> professionals focused on bipolar disorder: A global needs survey by the International Society for Bipolar Disorders
Bibliographic record
Abstract
INTRODUCTION: The International Society for Bipolar Disorders created the Early Mid-Career Committee (EMCC) to support career development of the next generation of researchers and clinicians specializing in bipolar disorder (BD). To develop new infrastructure and initiatives, the EMCC completed a Needs Survey of the current limitations and gaps that restrict recruitment and retention of researchers and clinicians focused on BD. METHODS: The EMCC Needs Survey was developed through an iterative process, relying on literature and content expertise of workgroup members. The survey included 8 domains: navigating transitional career stages, creating and fostering mentorship, research activities, raising academic profile, clinical-research balance, networking and collaboration, community engagement, work-life balance. The final survey was deployed from May to August 2022 and was available in English, Spanish, Portuguese, Italian, and Chinese. RESULTS: Three hundred participants across six continents completed the Needs Survey. Half of the participants self-identified as belonging to an underrepresented group in health-related sciences (i.e., from certain gender, racial, ethnic, cultural, or disadvantaged backgrounds including individuals with disabilities). Quantitative results and qualitative content analysis revealed key barriers to pursuing a research career focused on BD with unique challenges specific to scientific writing and grant funding. Participants highlighted mentorship as a key facilitator of success in research and clinical work. CONCLUSION: The results of the Needs Survey are a call to action to support early- and midcareer professionals pursuing a career in BD. Interventions required to address the identified barriers will take coordination, creativity, and resources to develop, implement, and encourage uptake but will have long-lasting benefits for research, clinical practice, and ultimately those affected by BD.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".