Factors Affecting Membership in Specialty Nursing Organizations
Why this work is in the frame
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Bibliographic record
Abstract
A discouraging trend in many specialty nursing organizations is the stagnant or declining membership. The research committee of the Southeast Texas Chapter of the Association of Rehabilitation Nurses (ARN) collected data and studied this trend to determine what changes would be necessary to increase membership. Using Herzberg's motivational theory as a framework, a review of the literature was initiated. There were few current studies on this issue, but relevant information was found about nursing's emerging workforce, as well as implications of the growth of magnet hospitals, which affect whether nurses join specialty nursing organizations. A multifaceted data-collection approach using convenience samples was designed. First, relevant literature was reviewed. Second, a survey was sent by e-mail to other ARN chapters. Third, a telephone survey on other specialty organizations in the geographic region was completed. Finally, members of the local ARN chapter and four other specialty organizations, as well staff nurses in the geographic area, were given questionnaires to complete. Descriptive statistics and cross tabulations were used to determine why nurses do and do not join specialty organizations (N = 81). The most frequent reasons for joining an organization were to increase knowledge, benefit professionally, network, and earn continuing education units. Reasons for choosing not to participate were family responsibilities, lack of information about these organizations, and lack of time. Ways to reverse the decline in membership are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it