Bibliographic record
Abstract
Occupational therapists open doors to occupation. The goal of occupational therapy is to enable people to engage in occupations that support and bring meaning to daily life. With this vision, we have a responsibility to create and use knowledge to ensure our commitment to the rights of all people to participate fully in daily life. Over the past 30 years, we have gained substantive knowledge from research from occupational therapy, occupational science and other disciplines. Sources of knowledge for occupational therapy come from person/people’s needs, values, dreams; therapists’ wisdom and reasoning; and research.In this paper, I examine how to use current knowledge and create the knowledge needed for the future. Specifically, I discuss dimensions of knowledge, knowledge creation frameworks, knowledge translation and the process of learning. We have a responsibility to build a knowledge creation process consistent with occupational therapy values. The creation and use of knowledge is complex and does not happen automatically. Given this complexity, we must guard against a focus on creating knowledge that applies universally and does not take into account culture, context and individual needs. Knowledge creation in occupational therapy can be a wonderful journey – bringing together experience and action to learn by doing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".