Implementation of a Humanoid Robot as an Innovative Approach to Child Life Interventions in a Children’s Hospital: Lofty Goal or Tangible Reality?
Bibliographic record
Abstract
Introduction This study reports the findings on how Child life specialists (CLSs) implemented an innovative approach to providing therapeutic support to pediatric patients. Methods Part of a larger study that uncovered themes about CLSs’ experiences while working with MEDi®, this study reports the reflections that CLSs have about the process of implementation. Seven CLSs participated in semi-structured interviews. Content analysis was conducted on interview data and three themes were generated. Results The first was in regards to the adoption process whereby CLS challenges, successes, and surprises were revealed. Second, CLSs explained how using MEDi® aligned with the roles and responsibilities of their profession. The third area of understanding was in CLS explanation of the friendly emotional impact MEDi® seems to have on the hospital environment. Conclusion Child life specialists are encouraged to use the MEDi® robot to support children at the bedside.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".