Leadership Practices of State Associations: Does State President Leadership Style Encourage Membership?
Bibliographic record
Abstract
Background: This research addressed the “graying” of the professional state occupational therapy association as new clinicians are more frequently making the decision not to join. It is particularly relevant for boards who are attempting to establish and retain members. Method: To assess the leadership styles of presidents of state occupational therapy associations and to examine the impact of leadership style on membership status, this research examined the self-perceived leadership styles of state occupational therapy association presidents, as measured by the Leadership Practices Inventory (LPI), a tool developed by Kouzes and Posner, authors of the Exemplary Leadership Model. It served to answer the following research questions: Is there a statistical difference between the Leadership Practices Inventory (LPI) score of state occupational therapy association presidents and the normed mean score of the LPI? And, is there a correlation between LPI mean scores of state occupational therapy association presidents and the respective state association’s membership representation of licensed occupational therapists and student therapists? This quantitative study used survey research design. Results: Sixty percent of state presidents participated. Evidence from this study indicates that state occupational therapy association presidents lead primarily from a transformational leadership style. In addition, those states whose presidents lead from this style demonstrate an overall higher membership status level than those who do not. Conclusion: The results of this study indicate that transformational leadership is related to increased membership status and is relevant to associations as they choose their leaders.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".