Aphasia and acute care: a qualitative study of healthcare provider perspectives
Bibliographic record
Abstract
Background Poor communication between patients with aphasia and healthcare providers has many adverse consequences. Training healthcare providers to support communication not only improves skill in communicating with people with aphasia, but also potentially avoids adverse incidents. However, families have reported negative communication experiences for their relatives with aphasia during early acute hospitalization and healthcare providers in acute hospitals have reported multiple barriers to managing people with aphasia.Aims To gather information from healthcare providers about their experiences and needs relative to managing people with aphasia in the acute hospital setting as part of a larger implementation project designed to 1) improve acute care staff communication with patients with aphasia, and 2) help staff provide support to families of people with aphasia.Methods Qualitative methods were employed to gather perspectives of stroke team staff regarding the management of aphasia in early acute care. Methods for collecting data from staff in a large metropolitan hospital in Ontario, Canada included open-ended surveys, observations on acute stroke units, interviews, and focus groups. Data were transcribed and analyzed to identify themes that defined staff experiences and needs relative to caring for people with aphasia in the acute setting.Results The overall finding was that staff found communication and caring for people with aphasia and their families in the acute context to be challenging and frustrating. Themes included identification of the following barriers: fast-paced context with competing priorities; limited staff familiarity with communication access or support; priority given to swallowing by speech-language pathologists; and difficulty in addressing the pressing need to provide support and information to family members of patients with aphasia. In addition, a pervasive need for communication with patients with aphasia as part of staff responsibilities was also identified as a theme.Conclusions Findings demonstrate the need for healthcare systems to support acute care staff in their efforts to provide care for people with aphasia. Finding effective ways to influence system change is a priority and is in line with the rationale for the larger implementation project designed to improve staff and patient/family experiences in acute care.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".