The Eternal Present: A Photovoice Study of the Experience of Geriatrics Residents During the COVID-19 Pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, medical residents had the task of being the frontline of the response, being exposed to high risk of infection, increased clinical duty, and long and irregular working hours in highly restricted environments, increasing their levels of stress. We sought to expose the experiences of a group of geriatrics residents during this period of change in their professional and personal lives through the photovoice methodology. Thirteen participants were recruited and had 2 weeks to take photographs. The photographs were discussed in group meetings; the content of the conversations was transcribed and analyzed using interpretive description. Sixteen themes were identified. They were divided into personal life (11 themes) and life as a resident (5 themes). Adaptation was the main theme that came into discussion. The photographs and themes show how life changed for the participants, having a feeling of isolation, especially from their families, and highlighting their experiences as a team and community. While the pandemic, particularly at its beginning, was a period of uncertainty and a heavy load of work, it also provided learning and experience to this group of young physicians, which should not hide the fact that mental health concerns and burnout were a common situation. An online gallery was created which is publicly accessible.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".