Meaning perspective transformation following stroke: the process of change
Bibliographic record
Abstract
PURPOSE: Transformative Learning in an educational theory that posits that individuals learn and grow when their meaning perspectives (frames of reference for interpreting an experience based on knowledge, feelings, values and beliefs) are reformulated following a critical event. This theory has become quite influential in the exploration of adaptation to chronic illness. This study explored whether the change that occurs following stroke follows a process similar to transformative learning. METHOD: Grounded Theory approach was used to explore changes in meaning perspective among 12 people who were members of stroke support organisations, had a stroke at least 1 year prior to the study and described themselves as viewing life positively following stroke. Constant comparison analysis of interviews with these individuals was used to explore their experience following stroke. RESULTS: Meaning perspective transformation occurred with four factors contributing to transformation: triggers, support, knowledge and choices to action. A substantive grounded theory of the process of meaning perspective transformation following stroke is presented, which illustrates the interaction of these contributing factors in initiating and facilitating the transformation process. CONCLUSION: Transformative learning can offer insight into how people who have experienced stroke learn, rebuild competence and re-engage in valued activities.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".