Measuring the context of care in an Australian acute care hospital: a nurse survey
Bibliographic record
Abstract
BACKGROUND: This study set out to achieve three objectives: to test the application of a context assessment tool in an acute hospital in South Australia; to use the tool to compare context in wards that had undergone an evidence implementation process with control wards; and finally to test for relationships between demographic variables (in particular experience) of nurses being studied (n = 422) with the dimensions of context. METHODS: The Alberta Context Tool (ACT) was administered to all nursing staff on six control and six intervention wards. A total of 217 (62%) were returned (67% from the intervention wards and 56% from control wards). Data were analysed using Stata (v9). The effect of the intervention was analysed using nested (hierarchical) analysis of variance; relationships between nurses' experience and context was examined using canonical correlation analysis. RESULTS: Results confirmed the adaptation and fit of the ACT to one acute care setting in South Australia. There was no difference in context scores between control and intervention wards. However, the tool identified significant variation between wards in many of the dimensions of context. Though significant, the relationship between nurses' experience and context was weak, suggesting that at the level of the individual nurse, few factors are related to context. CONCLUSIONS: Variables operating at the level of the individual showed little relationship with context. However, the study indicated that some dimensions of context (e.g., leadership, culture) vary at the ward level, whereas others (e.g., structural and electronic resources) do not. The ACT also raised a number of interesting speculative hypotheses around the relationship between a measure of context and the capability and capacity of staff to influence it.We propose that context be considered to be dependent on ward- and hospital-level factors. Additionally, questions need to be considered about the unit of measurement of context in studies of knowledge implementation--is individual (micro), ward (meso) or hospital-level (macro) data most appropriate? The preliminary results also raise questions about how best to utilise this instrument in knowledge translation research.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".