SWOT analysis of a pediatric rehabilitation programme: A participatory evaluation fostering quality improvement
Bibliographic record
Abstract
PURPOSE: To present the results of a strengths, weaknesses, opportunities and threats (SWOT) analysis used as part of a process aimed at reorganising services provided within a pediatric rehabilitation programme (PRP) in Quebec, Canada and to report the perceptions of the planning committee members regarding the usefulness of the SWOT in this process. METHOD: Thirty-six service providers working in the PRP completed a SWOT questionnaire and reported what they felt worked and what did not work in the existing model of care. Their responses were used by a planning committee over a 12-month period to assist in the development of a new service delivery model. Committee members shared their thoughts about the usefulness of the SWOT. RESULTS: Current programme strengths included favourable organisational climate and interdisciplinary work whereas weaknesses included lack of psychosocial support to families and long waiting times for children. Opportunities included working with community partners, whereas fear of losing professional autonomy with the new service model was a threat. The SWOT results helped the planning committee redefine the programme goals and make decisions to improve service coordination. SWOT analysis was deemed as a very useful tool to help guide service reorganisation. CONCLUSIONS: SWOT analysis appears to be an interesting evaluation tool to promote awareness among service providers regarding the current functioning of a rehabilitation programme. It fosters their active participation in the reorganisation of a new service delivery model for pediatric rehabilitation.
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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 | 0.116 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".