Internalisatie van de oplossingsgerichte werkwijze door de zorgprofessional in de gehandicaptenzorg: Het verloop ervan en factoren die er invloed op hebben
Bibliographic record
Abstract
Aims: This paper describes the learning process of the solution-focused approach of 10 care professionals, who are working with people with an intellectual disability, over a period of one year. This study examined the influence of work experience and training sessions on the internalization of solution-focused principles in the work of the healthcare professional. Background: The solution-focused approach differs from the traditional problem oriented approach in many aspects. The solution-focused approach aims to shift the focus of interaction away from the traditional concentration on an individual’s problem and weaknesses towards the identification of their strengths and positive coping mechanisms. Because of the differences between the two approaches, professionals need to be trained in this new way of working. Design and methods: Ten care professionals were asked to describe a specific working moment in relation to the solution-focused approach every two weeks. To explore the learning process, which is operationalized in term of the number of described solution-focused aspects, the data were analyzed based on a theoretical concept of the solution-focused approach. For every care professional an individual diagram was designed which reflected the number of solution-focused aspects per working moment described. Results and findings: The number of described aspects of every care professional was fluctuating and didn’t indicate an increased number of solution-focused aspects. With respect to the trainings, a positive impact of the first training was observable in regard to an increased number of described solution-focused aspects. Furthermore, the results show differences of described solution-focused techniques in relation to work experience. Conclusion: During the period of one year, there are no clear differences notable with regard to the number of described aspects. The work experience seems to have a minimal relation with the internalization of solution-focused aspects in work applications of the professionals. Also the whole training program doesn’t show clear effects on the learning process. However, this study provides some insights into the individual realization of the solution-focused approach.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".