Humanization of care for adult ICU patients: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to review and map data from studies on the humanization of care in intensive care units based on a conceptual framework. INTRODUCTION: The critical context of adult patients in intensive care units directs the focus on clinical medical interventions, drawing attention away from the humanization of care. Rethinking intensive care is imperative and the humanization of care is an absolute priority. Literature on this topic is scattered under umbrella terms such as "patient-centered care" or specific terms referring to target interventions. A scoping review based on a conceptual framework will map the literature on humanization in intensive care units. INCLUSION CRITERIA: The review will consider studies on the humanization of care, or on one of the seven components of the conceptual framework (communication, visiting hours, patients' and health care professionals' well-being, family involvement and satisfaction, post-intensive care unit syndrome, and physical environment) focusing on adult patients in intensive care units. Studies on co-existing states or end-of-life care will be excluded. METHODS: A search of Embase, PubMed, and CINAHL will be conducted to identify relevant studies in English or French on the humanization of care, holistic care, or person-centered care in an adult intensive-care environment. The time period covered will be January 1, 2010 to the present. An iterative team approach will be used to screen and select studies and extract data. Data will be charted according to study design, distribution in time, cultural context, concepts, use of theoretical models, and intervention. The results will be summarized and reported, identifying the implications for policy, practice, and research.
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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 | 0.119 | 0.081 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
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".