Experiential Community Health Assessment Through PechaKucha
Bibliographic record
Abstract
BACKGROUND: Nursing education is a robust vehicle for change for community health nursing (CHN) in undergraduate programs. Nurses with a broad range of CHN competencies will be needed to meet the demands of community-based care in the coming years. METHOD: To meet the changing curricular demands around CHN, an experiential learning opportunity presented itself with the use of PechaKucha to support students. This experiential approach to learning the Canadian Community Health Nursing Professional Practice Model & Standards of Practice using a windshield and walkabout survey resulted in the students presenting the findings through the PechaKucha method. RESULTS: Kolb's experiential learning theory served as the theoretical foundation. The experiential application of the Canadian Community Health Nursing Professional Practice Model & Standards of Practice helps to create interest in CHN and develop future competent and confident community nurses. CONCLUSION: By discovering CHN applications through experiential learning, students are in a better position to understand the scope and role of CHN practice. [J Nurs Educ. 2018;57(9):566-569.].
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".