Twelve tips to combat ill-being during the COVID-19 pandemic: A guide for health professionals & educators
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Background: Self-determination theory (SDT) represents an organismic theory of motivation and well-being, viewing people as naturally evolving creatures with innate needs for growth, mastery, and connection. According to SDT, for these tendencies to function optimally and for people to flourish, they require support of three basic psychological needs-autonomy, competence, and relatedness. During a pandemic such as the coronavirus disease 2019 (COVID-19), which can provoke isolation, fear, and feelings of helplessness, it is more important than ever to prioritize and support each other's basic psychological needs. Aim: The concept of basic psychological need satisfaction is relevant in the health professions, but during a crisis, it is easy for these needs to get overlooked or thrown aside. Through this article, we aim to make this concept more understandable and applicable by those in the health and education professions, including students. Methods: SDT literature was foundational to creating these practical guidelines. Results: The authors present 12 SDT-derived tips for practitioners, educators, administrators, and learners, on ways to engage in need-supportive behaviour and promote well-being during the COVID-19 pandemic. Conclusion: These tips demonstrate that going back to the basics in times of emergency and stress can help optimize outcomes while fostering connection, ability, and purpose. They can be learned through practice and applied to anything, from emails and social media, to teaching, to patient care.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.022 | 0.020 |
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".