Bouncing Beyond Adversity in Oncology: An Exploratory Study of the Association Between Professional Team Resilience at Work and Work-Related Sense of Coherence
Bibliographic record
Abstract
Team resilience at work (TR@W) is an important resource for bouncing beyond adverse situations. Adopting a health-promoting salutogenic approach, this cross-sectional study explores whether oncology team resilience, which is significantly associated with work-related sense of coherence (Work-SoC), and examines the roles of team member characteristics, quality of work life, and perceived impact of COVID-19. Team members (n = 189) from four oncology settings in Québec (Canada) completed self-administered e-questionnaires. Structural equation modeling was used to identify the best-fitting model and significant relationships among study variables. The results showed a significant positive reciprocal relationship between TR@W and Work-SoC (R = 0.20) and between Work-SoC and TR@W (R = 0.39). These two variables were influenced by gender, gender roles, age, or COVID-19. The resulting model confirms our initial assumption that a higher level of TR@W is significantly associated with a more positive Work-SoC. Our findings provide new insights into subscale items perceived positively by oncology team members, such as perseverance, connectedness, and capability; and identify areas, such as self-care, within the team that may require greater attention to bounce beyond adversity. They also suggest there may be different levels (individual, team, and organizational) of resources under the health salutogenic umbrella.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".