Room for Play: An Evaluation of the Lost [Healing] Spaces in Pediatric Healthcare Facilities
Bibliographic record
Abstract
The essence of space within pediatric healthcare has been governed through a catalyst of distinct criteria.Although these criteria are functional for medical and research practice requirements associated with hygiene and health, they should also reflect healing therapeutics and a sense of serenity.During our turbulent times, the COVID-19 pandemic has evolved the way healthcare is procured and practiced.The built environment provides a place for people to live, work, and partake in their day-today activities.However, the built environment of the spaces for pediatric healthcare lacks the "room for play" in their built fabric, to such a degree as lost spaces.These "rooms for play" are synonymous for spaces for self-reflection, places for thinking, relaxing, grieving, celebration, and not lost child's play.It inspires confidence, encourages playfulness, evokes feelings of belonging and offers hope for healing.These multifunctional spaces have the potential to supplement the designated functions of the built healthcare environment of which they are a part.This thesis evaluates the possibility to integrate the spaces of healthcare with rooms for play that are conducive to an integral human perceptual response enhancing our overall sense of well-being and restorative healing.Case studies and literary research will support a design proposal for
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".