What Makes a Leader: Identifying the Strengths of Canadian Physical Therapists
Bibliographic record
Abstract
PURPOSE: To identify the personal strengths of Canadian physical therapists who hold leadership positions and compare them with the strengths of Canadian physical therapists who do not occupy positions of leadership. METHODS: A quantitative, cross-sectional online survey was distributed to registered Canadian physical therapists. We used the Clifton StrengthsFinder to evaluate 34 characteristics and determine which characteristics described a participant's strengths. Population demographics and leadership strengths were described via frequency distributions and percentages; chi-square analyses and Fisher's exact tests were used to compare differences between groups. RESULTS: Of 173 physical therapists who completed the survey, 108 occupied a position of leadership, and 65 did not. Those in the leader group had significantly more experience and achieved a higher level of education. Leaders most frequently exhibited the strengths of learner, achiever, responsibility, input, and strategic, whereas non-leaders most frequently displayed strengths of learner, achiever, input, relator, and harmony. Leaders were significantly more likely than non-leaders to possess the achiever strength. Gender, level of education, and years of experience did not significantly influence which strengths were present in the leadership profile. CONCLUSIONS: There is substantial overlap between leaders and non-leaders in terms of leadership profiles. Future research should investigate whether leadership strengths vary depending on the leadership position occupied and whether leadership development initiatives promote leadership strengths.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".