Resilience at Work among Healthcare Professionals in Oncology during and beyond the Pandemic: Report from A Deliberative Multi-Stakeholder Reflexive Symposium
Bibliographic record
Abstract
The chronic distress faced by healthcare professionals (HCPs) in oncology was exacerbated by the COVID-19 pandemic, heightening the need to improve their resilience. The Entretiens Jacques Cartier symposium provided an opportunity for participants from France and Quebec to share perspectives on resilience at work and discuss interventions at individual and organizational levels to support HCP health and well-being. Fifty-eight stakeholders were invited to the symposium, including HCPs, government decision-makers, researchers, and patient representatives. The symposium began with presentations on the nature of professional resilience at work in oncology and promising interventions developed in France and Quebec. Participants were then engaged in deliberation on how evidence and experiential knowledge could contribute to workplace strategies to strengthen resilience. Small-group reflexive sessions using the photovoice method, and an intersectoral roundtable, elicited the expression and deliberation of multiple perspectives on the nature and building blocks of resilience. Four main themes emerged from the discussions: (1) that resilience remains a muddy concept and can be associated pejoratively with “happycracy”; (2) that resilience must contend with bounded autonomy and captors; (3) that it relies on a sense of coherence at work; and (4) that patients play a role in improving HCP resilience. Stakeholders from healthcare systems in different countries view resilience at work as a means of equipping teams to handle chronic and punctual stresses in cancer care. The symposium emphasized the importance of better defining what resilience at work means and pursuing explorations of multicomponent interventions to support oncology HCPs and the patients they care for. The themes raised by participants at the symposium suggest pathways for furthering this exploration.
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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 | 0.051 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.039 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".