Applying Psychotherapeutic Principles to Bolster Resilience Among Health Care Workers During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has affected the entire globe with overwhelming speed and impact. The pandemic is both highly threatening and poorly understood, typical of deeply distressing conditions. Stress associated with uncertain recommendations from authorities, fear of illness and contagion for oneself and one's loved ones, extended periods of isolation, moral conflicts, financial instability, perception of discrimination and/or stigma, and ongoing loss and grief imperil mental health and resilience among the general population and high-risk groups. Health care workers (HCWs) face additional challenges that increase their vulnerability to distress and burnout. Bolstering resilience among HCWs can allow them to continue working with the intensity and focus their jobs require, which in turn supports the overall functioning of the health care system. Given their training in understanding wellness, distress, and psychotherapeutic treatment, mental health clinicians are well positioned to respond to this need. By studying the lessons from past and present experiences with public health emergencies and by incorporating principles from psychotherapeutic literature and training, clinicians can help facilitate an informed and effective response. The goal of this article is to discuss the development of a resilience coaching model that is rooted in principles from psychotherapeutic literature and practice to support psychological well-being among hospital-based HCWs. This model, developed to support the authors' health care colleagues working in a Toronto hospital, is generalizable, can be adapted for use by any mental health clinician, and makes explicit how previous training in psychotherapy may be applied to coaching and supporting frontline HCWs.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".