Fostering humanism: a mixed methods evaluation of the Footprints Project in critical care
Bibliographic record
Abstract
OBJECTIVES: The objectives of this mixed-methods study were to assess the uptake, sustainability and influence of the Footprints Project. SETTING: Twenty-two-bed university-affiliated ICU in Hamilton, Canada. PARTICIPANTS: ICU patients admitted and their families, as well as clinicians. INTERVENTIONS: We developed a personalised patient Footprints Form and Whiteboard to facilitate holistic, patient-centred care, to inform clinical encounters, and to create deeper connections among patients, families and clinicians. OUTCOME MEASURES: We conducted 3 audits to examine uptake and sustainability. We conducted semi-structured interviews with 10 clinicians, and held 5 focus groups with 25 clinicians; and we interviewed 5 patients and 13 family representatives of 5 patients who survived and 5 who died in the ICU. Transcripts were analysed using qualitative content analysis. RESULTS: The Footprints Project facilitated holistic, patient-centred care by setting the stage for patient and family experience, motivating the patient and humanising the patient for clinicians. Through informing clinical encounters, Footprints helped clinicians initiate more personal conversations, foster deeper connections and guide treatment. Professional practice influences included more focused attention on the patient, enhanced interdisciplinary communication and changes in community culture. Initially used in 15.8% of patients (audit A), uptake increased to 51.4% in audit B, and was sustained at 57.8% in audit C. CONCLUSIONS: By sharing valuable personal information about patients before and beyond their illness on individualised whiteboards at each bedside, the Footprints Project fosters humanism in critical care practice.
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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 | 0.103 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".