Diversity and development of Indigenous rehabilitation professional student identity
Bibliographic record
Abstract
Abstract Background In Canada, disparities between Indigenous and non-Indigenous Peoples continue to exist in health and education because of the past and current harms of racism and colonization. One step towards closing health gaps is clinicians who can provide health and social care services that are free of racism and mistrust. Indigenous health providers are in the best position to provide this culturally relevant and safe care to their own communities. Therefore, more Indigenous students graduating from health professional programs are required to meet these needs. Indigenous identity support can be a facilitator for Indigenous student academic success but developing one’s Indigenous identity can be challenging in post-secondary education environments. We explored how Indigenous rehabilitation students expressed, and wanted to be supported in their identity and academic success. Methods Using a narrative inquiry approach, we conducted interviews with seven students from the occupational, physical, and respiratory therapy programs of a Canadian university. Students were asked to tell their story of learning about, applying to, and being in their rehabilitation program and how their Indigenous identity impacted these experiences. Data analysis was conducted by Indigenous and non-Indigenous team members, analyzing the stories on interaction of the participant with (1) themselves and others, (2) time, and (3) situation or place. Results The researchers developed seven mini-stories, one for each participant, to illustrate the variation between participant experiences in the development of their Indigenous and professional identity, before and during their rehabilitation program. The students appreciated the opportunities afforded to them by being admitted to their programs in a Indigenous Peoples category, including identity affirmation. However, for most students, being in this category came with feared and/or experienced stigma. The work to develop a health professional identity brought even more complexity to the already complex work of developing and maintaining an Indigenous identity in the colonized university environment. Conclusion This study highlights the complexity of developing a rehabilitation professional identity as an Indigenous student. The participant stories call for universities to transform into an environment where Indigenous students can be fully accepted for their unique gifts and the identities given to them at birth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.010 |
| 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".