Bibliographic record
Abstract
Who is applying and who is being admitted to Ontario Master of Physical Therapy university programs?These are the two important questions that are answered by Coghlan and colleagues' study. 1 This unprecedented review and analysis is long overdue, and Coghlan and colleagues are to be commended for taking a comprehensive, thorough, and reflective approach to comparing Canadian population data with the demographic variables of all applicants to and students of selected Ontario English-language Master of Physical Therapy programs over a 10-year period.The authors are essentially asking (1) are we doing a good job of attracting applicants and admitting students considering provincial and national lenses on population demographics and (2) how can we do better?This study generated some very interesting findings.Aside from physical therapy's being a female-dominated profession, the demographics of applicants and students are mostly representative of the diverse Canadian population, although the data also indicate that the number of Aboriginal applicants does not reflect the Canadian population.From 2004 to 2014, the number of men applying to and being admitted to physical therapy programs significantly increased, which has resulted in an increase in the number of men practising physical therapy.A trend exists wherein the proportion of male students is slightly less than that of male applicants (36% vs. 33%; 2014 data 1 ).I'm interested in learning more about this.What might be some factors that contribute to this trend?For the most part, students applying to English-speaking Master of Physical Therapy programmes come from southern Ontario, typically from large urban population centres, and the proportion of students across geographical regions and population centre size is similar to that of applicants.As Coghlan and colleagues 1 noted, applicants and students from British Columbia outnumber those from all other provinces (other than Ontario); they offer potential explanations for this finding in their Discussion section.Across the 10 years of data, the proportion of total physiotherapy students who self-declared as Aboriginal was slightly higher than the proportion of total applicants who self-declared as Aboriginal.However, the proportion of applicants who self-declared as Aboriginal was less than that in the total population (4.3% in Canada and 2.4% in Ontario).2 The authors offer several potential hypotheses for this difference and also identify it as an area for future research.For example, should universities engage with Aboriginal communities to explore the potential of increasing efforts to recruit Aboriginal students to apply to Ontario physiotherapy programs, or should they specifically reserve seats for those who self-declare as Aboriginal and meet the entrance requirements?Efforts to increase the proportion of self-declared Aboriginal physical therapy students to match their proportion of the national population would also match the recommendations of Honouring the Truth, Reconciling for the Future: Summary of the Final Report of the Truth and Reconciliation Commission of Canada 3 that strategies be developed to eliminate education and employment gaps between Aboriginal and non-Aboriginal Canadians and to increase the number and retention of Aboriginal professionals working in the health care field.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.049 | 0.042 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".