Demands and Rewards of Working Within Multidisciplinary Teams in Pediatric Oncology: The Experiences of Canadian Health Care Providers
Bibliographic record
Abstract
Pediatric oncology care in Canada is delivered by multidisciplinary teams consisting of healthcare providers with different areas of expertise. Limited information is available on how the multidisciplinary team influences jobrelated rewards, demands, and stress in pediatric oncology. A qualitative approach was adopted to learn about healthcare providers’ experiences of working within a multidisciplinary team in pediatric oncology. Qualitative interviews were conducted with 33 healthcare providers (13 oncologists, 9 nurses, 5 social workers, and 6 child-life specialists) from four pediatric oncology centres. Topics explored included: demands and rewards associated with how the multidisciplinary team worked; description of one’s area of expertise; and healthcare provider’s responsibilities. Thematic analysis was used to identify sources of demands and rewards of working in a multidisciplinary team. Healthcare providers described rewards of working within a multidisciplinary team in three areas: sharing expertise and collaboration; giving and receiving social and emotional support; and being valued by and valuing team members. Healthcare providers discussed demands of working within a multidisciplinary team in four areas: interpersonal and communication tensions; conflicting views about providing care; role confusion, overlap and being undervalued; and hospital environment. These findings may inform interventions that alleviate healthcare provider stress and promote strategies that lead to greater job satisfaction.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".