Object Therapy: A Student-selected Component Exploring the Potential of Museum Object Handling as an Enrichment Activity for Patients in Hospital
Bibliographic record
Abstract
This study involved innovative research in a novel field, namely ‘object therapy’, within the framework of astudent-selected component (SSC) undertaken by second year, Phase 1 Medicine students at University College London.The project had a series of intrinsic aims: to provide medical students with communication skills, methods of assessingwellbeing and research techniques and to evaluate the potential of museum object handling as an enrichment activity inhospitals. Five medical students undertook museum object handling activities at 24 patient’s bedsides in order to assessthe psychological impact of ‘object therapy’ using standard Quality of Life (QoL) measures. Quantitative analysesindicated an increase in overall well being and patient’s perception of their health status at the end of the session.Qualitative analyses revealed patients felt positive about the role of object handling sessions as a distraction fromeveryday ward life. The study showed that this is a novel enrichment activity which has the potential to enhancepatient’s lives whilst in hospital. The SSC provided valuable communication and research skills to students in theirpre-clinical year, affording an opportunity to interact with patients, gain ward experience and an appreciation of theimportance of considering the whole person when treating a patient.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| 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".