Occupational therapy contributions to people with neurological conditions: Time to set research priorities
Bibliographic record
Abstract
People with neurological conditions experience many challenges in their daily lives, ranging from basic activities of daily living through to more complex occupations such as driving a car, maintaining paid employment, or going on vacation with family or friends. Challenges evolve over time for many reasons – progression of the condition (in some cases), normal aging, new responsibilities at home or work, residential moves, or shifts in social or financial circumstances, just to name a few. The potential for occupational therapists to make a positive impact on the lives of people with neurological conditions is high, given our focus on enabling occupation. But what does the evidence say? A quick search of the Cochrane Library using the term ‘occupational therapy’ uncovered a total of 49 reviews, five of which explicitly addressed the effectiveness of occupational therapy for people with neurological conditions (multiple sclerosis, stroke, Parkinson’s disease). Only one review, which focused on people with stroke (Legg et al., 2006), offered a positive conclusion about occupational therapy interventions. The authors found that ‘for every 11 (95%, CI 7 to 30) patients receiving an occupational therapy intervention to facilitate personal activities of daily living, one patient was spared a poor outcome’ (Legg et al., 2006: 2). The four remaining Cochrane reviews all concluded that there was insufficient evidence to support occupational therapy interventions for the specific population targeted (that is, people with multiple sclerosis, people with stroke in care homes, people with cognitive impairment from stroke, people with Parkinson’s disease). Each group of authors noted the lack of high-quality randomized trials for inclusion in the review process. There may be many reasons that occupational therapy researchers are not conducting these trials, but it is clear that the time has come to address them. One option is to model the research priority setting meetings hosted by the National Multiple Sclerosis Society in the United States (National Multiple Sclerosis Society, 2015), which are used to set a path forward for researchers and funders around a specific area of concern. Of note is that these meetings include people with the condition, researchers, funders, and other key stakeholders from around the world. Two other quick searches were conducted to identify sources of synthesized evidence about occupational therapy for people with neurological conditions – one in the Campbell Collaboration Library (www.campbellcollaboration.org) and one in OTseeker (www.otseeker.com). What was interesting about the findings from both of these sources was that the relevant reviews were about specific interventions occupational therapists might employ (for example, electronic assistive technology) rather than about occupational therapy per se. Several reviews concluded that the intervention was effective. The conclusions focused on the intervention rather than the delivering discipline. The findings from these three simple searches point to the need for more focused efforts to set research priorities about occupational therapy for people with neurological conditions. In addition, the differences across the three searches raise some interesting questions about the ways in which evidence is summarized and communicated, and what the implications might be for different stakeholders. Should we be examining the effectiveness of the discipline (which presumably uses a range of interventions) or the effectiveness of a specific intervention (which might be employed by several different disciplines treating people with neurological conditions)? To what extent should we be comparing the outcomes of specific interventions delivered by occupational therapists with the outcomes of other disciplines using the same intervention – if the evidence is
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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 | 0.104 | 0.235 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.020 | 0.011 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.019 | 0.042 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.020 | 0.020 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".