Factors Affecting Membership in Specialty Nursing Organizations
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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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".