The Influence of a Move to Program Management on Physical Therapist Practice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The purpose of this qualitative study was to examine how a move to program management (PM) from a traditional department structure affected the professional practice of physical therapists in a large Canadian teaching hospital. SUBJECTS: Twenty-five physical therapists participated in 1 of 5 focus groups, and 4 physical therapists participated in individual interviews. METHODS: Focus groups and structured interviews were conducted by an experienced facilitator who was not a physical therapist. All focus groups and interviews were audiotaped and transcribed. Using an open-coding technique, 2 investigators undertook line-by-line analysis of each transcript to identify and code specific events related to the physical therapists' experiences. The investigators reached a consensus on all coding categories and then identified themes. RESULTS: Seven themes that addressed issues of affect (a sense of loss, low morale, and positive coping), professional practice (loss of professional development activities, professional advantages, the assuming of multiple roles), and patient care were identified. DISCUSSION AND CONCLUSION: Physical therapists who were deployed from a department to a program described both positive and negative effects of the move to PM on their practice. There were reported influences on their personal affect, professional practice, and patient care. Staff and physical therapy administrators need to be aware of potential implications of an organization's move to PM on the professional practice of frontline staff.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".