An iterative approach to promoting departmental wellbeing during <scp>COVID</scp>‐19
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Addressing wellbeing among learners, faculty, and staff during the COVID-19 pandemic is a challenge for many clinical departments. Continued and systemic supports are needed to combat the pandemic's impact on mental health and wellbeing. This article describes an iterative approach to conducting a needs assessment and implementing a COVID-19-related wellness initiative in a psychiatry department. METHODS: Development of the initiative followed the Plan-Do-Study-Act (PDSA) quality improvement cycle and was informed by Shanafelt and colleagues' framework for supporting healthcare workers during the COVID-19 pandemic. Key features included the establishment of a Wellness Working Group, the curation of relevant resources on the Department's website, and the deployment of regular, monthly surveys that informed the creation of further supports, such as a weekly online drop-in support group. RESULTS: Survey response rates ranged from 22% to 32% (n = 90-127) throughout our initiative. Across multiple surveys, approximately 80% of respondents reported feeling supported or very supported by the Department, and 90% were satisfied or very satisfied with the quantity and quality of information provided. Our support group and resources page were accessed by nearly one-quarter and one-third of respondents, respectively, with satisfaction rates of 81% or higher. Consistent with the Department's mandate, ensuring equity was a key focus of the Working Group throughout its operations. CONCLUSIONS: There is potential for this model to be scaled to create a faculty-wide, institution-wide, or regional approach to addressing wellbeing. Other departments may also wish to adopt similar approaches to supporting their members during this challenging time.
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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 | 0.077 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.003 | 0.008 |
| 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".