Physical and occupational therapy service delivery models for populations identified as hard-to-reach: A scoping review
Bibliographic record
Abstract
BACKGROUND: The delivery of rehabilitation services for hard-to-reach populations (e.g., refugees) is highly complex. There is a need for evidence-based approaches to deliver physiotherapy (PT) or occupational therapy (OT) services to this underserved group. OBJECTIVES: The purpose of this scoping review was to identify PT and OT service delivery models that have been implemented, for populations typically identified as hard-to-reach and their associated health outcomes. ELIGIBILITY CRITERIA: Articles were eligible if they described PT and/or OT services for hard-to-reach populations. There were no restrictions on study design. STUDY SELECTION: Six electronic databases (AMED, CINAHL, MEDLINE, EMBASE, Healthstar, and PsycINFO) were searched from January 2000 to June 2023. Articles were screened in duplicate by two independent reviewers, and conflicts were resolved by consensus. RESULTS: Twenty-one articles with variable sample sizes (min, max n = 3 to 237) were included and detailed PT and/or OT services for immigrants/migrants, refugees, hard-to-reach veterans, people experiencing homelessness, lower incomes, trauma/torture, and those living in rehabilitation-deficient areas. Common rehabilitation needs (e.g., clinician to client connectivity), barriers (e.g., high transportation costs) and facilitators (e.g., encouragement) were identified among the various populations, mainly due to intersecting identities such as those who are both traumatized and refugees. Unique factors pertaining to the PT and OT services were also identified in some groups, including access to child and family services for people experiencing homelessness. CONCLUSIONS: Despite common and individual needs, barriers, and facilitators in hard-to-reach groups in the literature, there is a need for studies with larger sample sizes, rigorous methodology and a conscious effort to publish the results of interventions to generate stronger recommendations for practice.
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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 | 0.021 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".