Honor society membership retention strategies: Promoting membership benefits from induction through transition to professional practice
Bibliographic record
Abstract
Being a member of an honor society is a long held tradition for recognition of student academic success. Honor societies have numerous benefits that include not only a level of prestige, but opportunities for continuing education, and networking for professional career advancement. The growth of online learning and education has changed the classroom to a virtual environment where a student’s learning achievement may go unrecognized since many honor societies tend to focus on traditional in class students. Recognizing the importance of an honor society and the need for a continued custom to acknowledge student excellence, a small private university in Southwestern Pennsylvania created a virtual nursing honor society that has become a chapter within an international honor society. This honor society chapter encompasses both online and on ground learners. Although a positive asset to students, the chapter has encountered challenges with membership retention after degree completion. Honor society membership retention is possible and enhanced by offering members tangible benefits. Retaining student membership in an honor society can significantly impact professional development as they transition to and grow within nursing practice. This is accomplished through contribution to knowledge development and use of evidence based practice. Therefore, the premise of this article is to share the strategies used by the honor society chapter and identify future recommendations for membership sustainability. The strategies presented in this article are applicable to both new and established honor societies, and professional organizations experiencing membership attrition.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".