The implications of the Canadian Stroke Best Practice Recommendations for design and allocation of rehabilitation after hospital discharge: a problematization
Bibliographic record
Abstract
Introduction: Implementation of the Canadian Stroke Best Practice Recommendations has improved inpatient rehabilitation. As attention is turned to the design and allocation of rehabilitation after hospitalization, examination of their implications for post-discharge rehabilitation could help optimize service planningMethods: Critical discourse analysis modeled on Alvesson and Sandberg’s method of problematization was conducted to determine how the Canadian Stroke Best Practice Recommendations envision and shape post-discharge rehabilitation, and identify any tensions and potential ways to resolve them.Results: Within the Canadian Stroke Best Practice Recommendations post-discharge rehabilitation is implicitly viewed as a continuation of inpatient rehabilitation. Rehabilitation is largely envisioned as a set of biomedical procedures aimed at normalization through correction of impairment. There is potential tension between this implicit goal and the explicit goal of providing patient and family-centered care and promoting reengagement in valued activities and roles.Conclusion: An alternate vision of post-discharge rehabilitation could help resolve this tension. Post-discharge rehabilitation could be envisioned as a self-management intervention. Rather than primarily an expert-driven process of measuring impairment and applying procedures aimed at normalization, rehabilitation would be considered facilitation of self-management with the goal of reengaging in forms of participation that comprise a satisfying life.Implications for RehabilitationImplicit assumptions within best practice guidelines powerfully influence recommendations. These ideas are difficult to examine because they seem self-evident.Implicit assumptions in the Canadian Stroke Best Practice Guidelines envision post-discharge stroke rehabilitation as an expert-driven, impairment-focused biomedical procedure.This biomedical image makes it difficult to provide care that meets the guideline’s explicit goals of client- and family-centeredness.Reimagining post-discharge stroke rehabilitation as a chronic self-care management intervention aimed at developing a satisfying life after stroke could improve patient 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 | 0.095 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.034 | 0.038 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".