Exploration of the Perceived Value of Self-Leadership Capacity in Physical Therapist Clinicians
Bibliographic record
Abstract
Purpose: Self-leadership, or leading oneself, is a critical component of leadership linked to improved individual performance, team effectiveness, and patient and organizational outcomes. This qualitative study explores physical therapist clinicians’ perceived value of self-leadership in their clinical practice and their interactions with inter-professional team members. Method: Purposeful sampling was used to recruit participants via the maximum variation strategy. Eleven licensed physical therapist clinicians with 2–39 years of clinical experience in five different clinical settings participated. Participants engaged in individual, semi-structured interviews that were audio-recorded and transcribed verbatim. Study rigour was addressed through the use of multiple coders, memoing, and member checking. Results: Four main themes emerged: developing expertise, pursuing professional growth, discovering and amplifying voice, and advocacy. The “why” behind the actions of those interviewed was to influence patient outcomes and quality of care and to address role identity as a physical therapist across multiple contexts. Conclusions: This is the first known exploration of the perceived value of self-leadership capacity in physical therapist clinicians. The study results suggest that leadership must be reframed as self-leadership, the precursor to leading others, so physical therapist clinicians can lead at all levels in everyday practice.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".