Impact of unit design on intensive care unit clinicians: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: This scoping review aims to identify the known impact of unit design on intensive care unit clinicians, and more specifically, to explore similarities and differences across critical care settings. INTRODUCTION: Construction and infrastructure renewal represent great opportunities for designing units that enhance patient care, as well as support the work of clinicians. A growing body of evidence is showing how unit design can impact clinical staff, but no reviews have been found that focus exclusively on clinicians within intensive care units. INCLUSION CRITERIA: The review will consider studies that include healthcare staff who offer direct patient care in adult or pediatric intensive care units. Studies that focus on the impact of design (related to physical environment features) on clinicians will be included. METHODS: The proposed systematic review will be conducted in accordance with JBI methodology for scoping reviews. The search strategy aims to find published and unpublished studies. The databases to be searched will include Embase MEDLINE, PsycINFO, Healthstar and CINAHL. Retrieved studies will be assessed against the inclusion criteria by two independent reviewers. For the papers included in the scoping review, data will be extracted and quality assessed by two independent reviewers. The extracted data will be presented in tabular form, and a narrative summary will describe how the results relate to the review objective.
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 | 0.132 | 0.103 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.017 |
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".