Attractiveness of employment sectors for physical therapists in Ontario, Canada (1999-2007): implication for the long term care sector
Bibliographic record
Abstract
BACKGROUND: Recruiting and retaining health professions remains a high priority for health system planners. Different employment sectors may vary in their appeal to providers. We used the concepts of inflow and stickiness to assess the relative attractiveness of sectors for physical therapists (PTs) in Ontario, Canada. Inflow was defined as the percentage of PTs working in a sector who were not there the previous year. Stickiness was defined as the transition probability that a physical therapist will remain in a given employment sector year-to-year. METHODS: A longitudinal dataset of registered PTs in Ontario (1999-2007) was created, and primary employment sector was categorized as 'hospital', 'community', 'long term care' (LTC) or 'other.' Inflow and stickiness values were then calculated for each sector, and trends were analyzed. RESULTS: There were 5003 PTs in 1999, which grew to 6064 by 2007, representing a 21.2% absolute growth. Inflow grew across all sectors, but the LTC sector had the highest inflow of 32.0%. PTs practicing in hospitals had the highest stickiness, with 87.4% of those who worked in this sector remaining year-to-year. The community and other employment sectors had stickiness values of 78.2% and 86.8% respectively, while the LTC sector had the lowest stickiness of 73.4%. CONCLUSION: Among all employment sectors, LTC had highest inflow but lowest stickiness. Given expected increases in demand for services, understanding provider transitional probabilities and employment preferences may provide a useful policy and planning tool in developing a sustainable health human resource base across all employment sectors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".