Bibliographic record
Abstract
Although objective measures of leadership may not exist, and leadership can be defined in many different ways, it is a construct that we understand intuitively when we see it, experience it, and are affected by it.Yet leadership in health care, and the qualities and attributes of leadership that enable people to support health system advancement, remains an elusive notion.The literature is replete with leadership theories, models, and approaches from different health disciplines aimed at identifying those attributes that can be learned, adopted, and promoted.Chan and colleagues 1 acknowledge the benefits of leadership exploration for professionals, patients/clients, and the health system as a whole; their study is a first exploration of the personal strengths of Canadian physical therapists in leadership positions.Another recent article on leadership in physical therapy argues that the ongoing need for health system change and improvement requires all health professionals to engage in leadership, whether or not they naturally identify as leaders. 2 Other authors 3,4 have also suggested that clinicians are in an ideal position to serve in leadership relationships within networks of peers, within organizations, with patients or clients, and within the broader health system.Shared or collective leadership has also been identified as a necessary strategy for health system change.[5][6][7][8][9] All these perspectives affirm how important it is for physical therapy, as a profession, to better understand the strengths we bring to the agenda of health system leadership.Particularly worthy of further discussion, in my view, is Chan and colleagues' finding that the leadership strengths of leaders and non-leaders in their sample of Canadian physical therapists overlap.Indeed, leaders do not always hold formal leadership roles, and certainly those in formal leadership roles are not always leaders; thus, this finding may simply reflect the foundational strengths that are characteristic of physical therapists in general, as well as those attributes that are carried forward in different leadership domains, both formal and informal.The substantial overlap in the leadership strengths of physiotherapy leaders and non-leaders may also suggest that most physical therapists possess the ability to lead.Chan and colleagues have opened the door to a long-overdue dialogue within our profession.Perhaps this dialogue should now evolve from identifying leadership strengths to acting on what we have always intuited to be true: that physical therapists are leaders.In the end, whether leaders are born or made, it would appear that those admitted to physiotherapy programmes seem predisposed to leadership.So perhaps the question now is, how will we augment those leadership strengths through entryto-practice and continuing professional development?The ''harmony'' and ''relator'' strengths identified among the non-leader physical therapists in Chan and colleagues' sample 1 align well with the model of servant leadership described by Trastek and colleagues, who cite the leadership focus as ''serving the highest needs of others'' toward their goals.3(p.379)There is tremendous merit in the ''harmony'' and ''relator'' strengths, which arguably fall within the identified qualities and characteristics of servant leadership consistent with empathy, healing, and awareness.3 McGowan and Stokes 2 call for future research investigating the leadership styles used by physical therapists,
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.079 | 0.058 |
| Insufficient payload (model declined to judge) | 0.012 | 0.013 |
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".