Building resilience in oncology teams: Protocol for a realist evaluation of multiple cases
Bibliographic record
Abstract
BACKGROUND: Teams caring for people living with cancer face many difficult clinical situations that are compounded by the pandemic and can have serious consequences on professional and personal life. This study aims to better understand how a multi-component intervention builds resilience in oncology teams. The intervention is based on a salutogenic approach, theories and empirical research on team resilience at work. This intervention research involves partnership between researchers and stakeholders in defining situations of adversity and solutions appropriate to context. METHODS: The principles of realist evaluation are used to develop context-mechanism-outcome configurations of a multi-component intervention developed by researchers and field partners concerned with the resilience of oncology teams. The multiple case study involves oncology teams in natural contexts in four healthcare establishments in Québec (Canada). Qualitative and quantitative methods are employed. Qualitative data from individual interviews, group interviews and observation are analyzed using thematic content analysis. Quantitative data are collected through validated questionnaires measuring team resilience at work and its effect on teaming processes and cost-effectiveness. Integration of these data enables the elucidation of associations between intervention, context, mechanism and outcome. DISCUSSION: The study will provide original data on contextual factors and mechanisms that promote team resilience in oncology settings. It suggests courses of action to better manage difficult situations that arise in a specialized care sector, minimize their negative effects and learn from them, during and after the waves of the pandemic. The mechanisms for problem resolution and arriving at realistic solutions to professional workforce and team effectiveness challenges can help improve practices in other settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.103 | 0.103 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.077 | 0.013 |
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".