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 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".