Abstract WP408: Tacls: Taking Action for Optimal Community and Long Term Stroke Care
Bibliographic record
Abstract
Introduction: In Canada, approximately 12% of acute stroke patients are admitted to long-term care (LTC; or residential aged care) facilities following an acute stroke event. An additional 20-30% of patients are discharged home from hospital with referral for community-based homecare. Training programs for health care providers in these settings is variable and at times inconsistent with best practices. Internationally, focus is now shifting from a predominant inpatient acute care focus, to one encompassing ongoing care and support in the community for people living with stroke. In 2015, an educational resource called Taking Action for Optimal Community & Long Term Stroke Care (TACLS) was launched across Canada to ensure the appropriate knowledge and skills of front line care providers for stroke survivors in community and LTC facilities; the focus of this resource is on rehabilitation and recovery. Methods: The purpose of this interactive session is to introduce the TACLS resource and to engage health professionals in an examination of current international community based rehabilitation and recovery programs. The discussion/workshop will allow participants to examine, compare and contrast components of the TACLS program with programs being developed or offered elsewhere. Results: As health care providers helping stroke survivors live well and longer means investing in the use of best practice tools and resources that fit the local context and organizational practices. Bringing together international opinions and observations around post-stroke community care will allow cross-collaboration and inter-professional networking opportunities that ultimately will benefit patients living with stroke in community based settings. Discussion: As care shifts from hospital to community based settings, the importance of tools available to support stroke survivors in this area of the care continuum is essential. In Canada, utilizing the HSF education resource (TACLS) provides information to support community based health care providers working with people who have had a stroke in helping them achieve optimal outcomes, regain their best level of functioning, and live meaningful lives.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.069 | 0.023 |
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".