Open education resources to support the WHO nurse educator core competencies
Bibliographic record
Abstract
AIM: This paper describes an innovative approach to tackling the shortage of qualified nurse educators, which is a major constraining factor or 'bottle-neck' to improve the global supply of nurses, especially in low- and middle-income countries. BACKGROUND: The World Health Organization commissioned experts to develop Nurse Educator Core Competencies that describe expectations for this cadre of workers. In their deliberations, the WHO experts cited the challenges affecting the adoption of these competencies, particularly the lack of resources available for implementation. To address this specific challenge, a USA-based non-government organiization, Nurses International, has developed Open Education Resources (NI-OER) to support nurse educators with freely accessible curriculum materials and remote mentoring support. METHODS: This paper applies item analysis to consider how the NI-OER could assist higher education institutes and individual faculty members in meeting each of the WHO Nurse Educator Core Competencies. FINDINGS: The NI-OER is a good fit with six of the Nurse Educator Core Competencies and a partial fit with the other two. DISCUSSION: Congruence with the WHO Nurse Educator Core Competencies is an important validity check for the NI-OER. The ultimate goal of the NI-OER is to promote sustainable development through intermediate goals related to supporting faculty as they prepare nurses for current and future service needs. Technological solutions like the NI-OER cannot solve all aspects of a complex problem like the global nursing shortage but are an important tool. IMPLICATIONS FOR NURSING AND HEALTH POLICY: This resource has significant implications for nursing and health policy because it tackles several constraints to the global goal of increasing production and capacity of nurses. Combined with the organization's remote mentoring and communities of practice, the NI-OER appears to have the potential to support novice nurse educators with accessible, adaptable resources.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".