Curriculum mapping for AACN Essentials 2021 Adoption: Experiences of a faculty domain team in evolving understanding of person-centered care
Bibliographic record
Abstract
The American Association of Colleges of Nursing (AACN) periodically updates its Essentials guidelines to evolve nursing curriculum to meet global healthcare demands. The AACN 2021 Essentials outline core competencies that address diverse patient needs and promote safe and effective nursing care. Curriculum mapping is used to align nursing programs with the AACN Essentials. The purpose of this qualitative focus group was to engage faculty in identifying and describing the competencies and sub-competencies of AACN 2021 Essentials Domain Two, Person-Centered Care (PCC), through the application of an interactive activity within a domain team. The interactive activity involved faculty members reflecting on their perspectives by writing down their interpretation of PCC on flower petals. These petals were arranged onto a unified flower display representing the department's view on Person-Centered Care. This exploration occurred during a one-day curriculum mapping at Kennesaw State University Wellstar School of Nursing in North-Central Georgia. Data was obtained using the flower petal activity with a sample size of 24. A thematic analysis revealed that (n = 13) of faculty responded with an action term, 33% (n = 8) of faculty responded with an attitude term, and 13% (n = 3) of faculty responded with both action and attitude terms when describing their perception of the PCC domain. The results indicated that most faculty view the PCC domain in terms of attitudes more than demonstrable action. These findings may be used to encourage faculty to incorporate more attitude-based learning strategies into their courses while adopting the AACN 2021 Essentials.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".