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Record W2765583608 · doi:10.1111/1440-1630.12433

Knowledge translation in occupational therapy

2017· editorial· en· W2765583608 on OpenAlexaboutno aff
Sally Bennett

Bibliographic record

VenueAustralian Occupational Therapy Journal · 2017
Typeeditorial
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge translationTerminologyExperiential knowledgeKnowledge managementWarrantTacit knowledgeProcess (computing)Quality (philosophy)PsychologyMedical educationEngineering ethicsMedicineComputer scienceEpistemologyBusinessEngineering

Abstract

fetched live from OpenAlex

The gulf between what we know from research and what we do in practice is substantial. The existence of primary research, its synthesis and dissemination, although essential, are not sufficient for ensuring its enactment to improve health outcomes. The result is that our clients often may not receive optimal care. Knowledge translation seeks to address this problem. Knowledge translation has been described as a dynamic and iterative process that involves synthesis, dissemination, exchange and application of knowledge to improve health (Canadian Institutes for Health Research (CIHR) (2015). The field is characterised by conceptual challenges with for example, the terms knowledge translation, knowledge exchange, research utilisation and implementation variably being used to describe interrelated and overlapping perspectives, however each addresses the aim of closing the gap between research and practice. Despite this variation in terminology, the ‘knowledge’ to be translated ordinarily refers to recommendations from research, with many authors giving preference to high-quality, synthesised research such as systematic reviews or recommendations from clinical practice guidelines (Grimshaw, Eccles, Lavis, Hill & Squires, 2012). The rationale given for this is that individual studies may not be definitive enough on their own to warrant the efforts involved in changing practice. Even if this is the case, the pluralistic nature of knowledge should also be taken into account (Straus, Tetroe & Graham, 2013), as all forms of knowledge are needed to enact and advance our practice. It is also clear that the processes involved in knowledge translation could not take place without other forms of knowledge – experiential, tacit and strategic knowledge – all essential for negotiating the complexities of change within dynamic health-care environments. The targets of knowledge translation are many and varied. Within occupational therapy, the literature describes knowledge translation in the fields of stroke rehabilitation, vocational rehabilitation, mental health, falls prevention, assessments and interventions for children with cerebral palsy, and occupational therapy for people with dementia, to name a few. Much of this work has involved bringing about change in health professionals’ practice and/or the systems within which they work, neither of which is easy. A whole body of literature exists offering models and frameworks for guiding knowledge translation, many of which propose an analysis of the individual and contextual barriers to change, and application of tailored strategies to target known barriers. Also pivotal is the need for knowledge translation to be well-planned and to consider the perspectives of many different stakeholders. However, beyond understanding the methods for translating research findings into practice, it is also critical that research is developed with the end-use in mind. This requires not simply knowing who the end users may be, but extends to co-creation of knowledge. ‘Integrated knowledge translation’ where researchers and knowledge-users work together to shape the research process both prior to the commencement of research and beyond its completion (CIHR, 2015) is not a new idea; however, true involvement of consumers in co-creation of research and its translation is now increasingly valued. In short, knowledge translation is not straight forward, can be approached from many different perspectives, utilises multiple forms of knowledge, involves many different stakeholders and is a continuing process, but is an essential activity that we need all engage in. The Australian Occupational Therapy Journal will continue to support this effort.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.817
GPT teacher head0.708
Teacher spread0.109 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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".

Quick stats

Citations6
Published2017
Admission routes1
Has abstractyes

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