Experiences of student and trainee autism researchers during the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
Circumstances surrounding the COVID-19 pandemic have resulted in significant personal and professional adjustments. Students and trainees, including those in autism research, face unique challenges to accomplishing their training and career goals during this unprecedented time. In this commentary, we, as members of the International Society for Autism Research Student and Trainee Committee, describe our personal experiences, which may or may not align with those of other students and trainees. Our experiences have varied both in terms of the ease (or lack thereof) with which we adapted and the degree to which we were supported in the transition to online research and clinical practice. We faced and continue to adjust to uncertainties about future training and academic positions, for which opportunities have been in decline and have subsequently negatively impacted our mental health. Students and trainees' prospects have been particularly impacted compared to more established researchers and faculty. In addition to the challenges we have faced, however, there have also been unexpected benefits in our training during the pandemic, which we describe here. We have learned new coping strategies which, we believe, have served us well. The overarching goal of this commentary is to describe these experiences and strategies in the hope that they will benefit the autism research community moving forward. Here, we provide a set of recommendations for faculty, especially mentors, to support students and trainees as well as strategies for students and trainees to bolster their self-advocacy, both of which we see as crucial for our future careers. LAY SUMMARY: The COVID-19 pandemic has affected students and trainees, including those in autism research, in different ways. Here, we describe our personal experiences. These experiences include challenges. For example, it has been difficult to move from in-person to online work. It has also been difficult to keep up with work and training goals. Moreover, working from home has made it hard to connect with our supervisors and mentors. As a result, many of us have felt unsure about how to make the best career choices. Working in clinical services and getting to know and support our patients online has also been challenging. Overall, the pandemic has made us feel more isolated and some of us have struggled to cope with that. On the other hand, our experiences have also included benefits. For example, by working online, we have been able to join meetings all over the world. Also, the pandemic has pushed us to learn new skills. Those include technical skills but also skills for well-being. Next, we describe our experiences of returning to work. Finally, we give recommendations for trainees and supervisors on how to support each other and to build a strong community.
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".