Family bedside orientations: An innovative peer support model to enhance a culture of family-centred care at the Stollery Children’s Hospital
Bibliographic record
Abstract
This paper presents family bedside orientations, an innovative bedside peer support model for families of paediatric patients piloted in one unit at the Stollery Children's Hospital in Edmonton, Alberta. The model invites family members of former patients back to the hospital as volunteer peer mentors responsible for meeting one-on-one with current inpatient families to provide a listening presence, discuss patient safety practices and encourage families to participate in their child's care. Using qualitative and quantitative data collection methods, the model was evaluated over 1 year (December 2014 to December 2015). Data sources included peer mentor field notes (from 163 visits) detailing the number of family bedside orientations completed by peer mentors and how they interacted with families, as well as post-visit family (n=35) surveys, Hospital-Child Inpatient Experience Survey data, peer mentor (n=6) questionnaires, focus groups with unit staff (n=10) and interviews with members of the project leadership team (n=5). Our findings indicated that family bedside orientations became an established practice in the pilot unit and positively impacted family care experiences. We attribute these successes to championing and support from unit staff and our multidisciplinary project leadership team. We discuss how our team addressed family privacy and confidentiality while introducing peer mentors in the unit. We also highlight strategies used to integrate peer mentors as part of the staff team and enhance peer support culture in the pilot unit. Practical considerations for implementing this model in other paediatric environments are provided.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".