Barriers and Enablers to Implementing Clinical Treatment Protocols for Fever, Hyperglycaemia, and Swallowing Dysfunction in the Quality in Acute Stroke Care (QASC) Project—A Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: The Quality in Acute Stroke Care (QASC) trial evaluated systematic implementation of clinical treatment protocols to manage fever, sugar, and swallow (FeSS protocols) in acute stroke care. This cluster-randomised controlled trial was conducted in 19 stroke units in Australia. AIM: To describe perceived barriers and enablers preimplementation to the introduction of the FeSS protocols and, postimplementation, to determine which of these barriers eventuated as actual barriers. METHODS: Preimplementation: Workshops were held at the intervention stroke units (n = 10). The first workshop involved senior clinicians who identified perceived barriers and enablers to implementation of the protocols, the second workshop involved bedside clinicians. Postimplementation, an online survey with stroke champions from intervention sites was conducted. RESULTS: A total of 111 clinicians attended the preimplementation workshops, identifying 22 barriers covering four main themes: (a) need for new policies, (b) limited workforce (capacity), (c) lack of equipment, and (d) education and logistics of training staff. Preimplementation enablers identified were: support by clinical champions, medical staff, nursing management and allied health staff; easy adaptation of current protocols, care-plans, and local policies; and presence of specialist stroke unit staff. Postimplementation, only five of the 22 barriers identified preimplementation were reported as actual barriers to adoption of the FeSS protocols, namely, no previous use of insulin infusions; hyperglycaemic protocols could not be commenced without written orders; medical staff reluctance to use the ASSIST swallowing screening tool; poor level of engagement of medical staff; and doctors' unawareness of the trial. LINKING EVIDENCE TO ACTION: The process of identifying barriers and enablers preimplementation allowed staff to take ownership and to address barriers and plan for change. As only five of the 22 barriers identified preimplementation were reported to be actual barriers at completion of the trial, this suggests that barriers are often overcome whilst some are only ever perceived rather than actual barriers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".