Place Attachment Dimensions and Role Assessment in Modern Hospitals
Bibliographic record
Abstract
Hospitals play a significant role in people’s physical and psychological health, as well as medical and ducational researches of the experts. Therefore, hospitals’ importance is due to both health-care services and the educational effect. Many hospitals do not meet international standards due to oldness and inattention to space users. Based on the studies on place attachment, dimension, and effects, the paper attempts to promote place attachment in clinical spaces through creative exploration. Qualities such as place identity, emotional attachment, place dependence, social bonding, spatial behavior, and functional attachment are the research analysis criteria. The research methods is practical and descriptive-analytic evaluating the problem’s invisible horizons through a critical, objective, and concrete viewpoint and makes creative strategies and models possible which are responsive to different environmental dimensions especially human dimension. To study the criteria for valuation and space creation, the users and what they admire, associate, understand, and use and for which they have “sense of belonging” were also considered. The research results reveal that designing a modern hospital based on the promotion of place attachment results in the quick recovery of the patients, hospital staff’s job satisfaction, medical education improvement and also solutions to the enhancement of the environment quality of the hospital.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".