Developing and maintaining the resilience of interdisciplinary cancer care teams: an interventional study
Bibliographic record
Abstract
BACKGROUND: Providing care to cancer patients is associated with a substantial psychological and emotional load on oncology workers. The purpose of this project is to co-construct, implement and assess multidimensional intervention continuums that contribute to developing the resilience of interdisciplinary cancer care teams and thereby reduce the burden associated with mental health problems. The project is based on resources theories and theories of empowerment. METHODS: The study will involve cancer care teams at four institutions and will use a mixed-model design. It will be organized into three components: (1) Intervention development. Rather than impose a single way of doing things, the project will take a participatory approach involving a variety of mechanisms (workshops, discussion forums, surveys, observations) to develop interventions that take into account the specific contexts of each of the four participating institutions. (2) Intervention implementation and assessment. The purpose of this component is to implement the four interventions developed in the preceding component, assess their effects and whether they are cost effective. A longitudinal quasi-experimental design will be used. Intervention monitoring will extend over 12 months. The effects will be assessed by means of generalized estimating equation regressions. A cost-benefit analysis will be performed to assess the cost-effectiveness of the interventions, taking an institutional perspective (costs and benefits associated with the intervention). (3) Analysis of co-construction and implementation process. The purpose of this component is to (1) describe and assess the approaches used to engage stakeholders in the co-construction and implementation process; (2) identify the factors that have fostered or impeded the co-construction, implementation and long-term sustainability of the interventions. The proposed design is a longitudinal multiple case study. DISCUSSION: In the four participating institutions, the project will provide an opportunity to develop new abilities that will strengthen team resilience and create more suitable work environments. Beyond these institutions, the project will generate a variety of resources (e.g.: work situation analysis tools; method of operationalizing the intervention co-development process; communications tools; assessment tools) that other oncology teams will be able to adapt and deploy elsewhere.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".