How Pediatric Oncology Nurses Can Foster the Establishment of Trust With Children and Their Parents: A Review of the Literature
Bibliographic record
Abstract
BackgroundChildren with cancer and their parents experience high levels of stress and can struggle with their mental health during active treatment. Pediatric oncology nurses are well positioned to establish therapeutic relationships with children/parents and offer support. Since trust is the foundation of therapeutic relationships, the purpose of this review was to provide a comprehensive overview of how pediatric oncology nurses develop trust with children and their parents.MethodA sub-analysis of 28 articles retrieved from a large scoping review using the Joanna Briggs methodology was completed to synthesize evidence on how trust is established between pediatric oncology nurses and children/parents. Data from included studies were extracted and thematically analyzed to present key themes.ResultsTrust was foundational to the provision of quality, patient- and family-centered nursing care. Key themes include: relationships, communication, nurses' approach, nurses' expertise, parental support, partnership, and mistrust.DiscussionPediatric oncology nurses should be made aware of the trust-building strategies identified in this review. Findings from this review should be utilized to inform future research that further investigates how trust is established between pediatric oncology nurses and children/parents, including how trust creates an environment for establishing therapeutic relationships and the provision of psychosocial support.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".