Translating and validating the Finnish version of the Manchester Clinical Supervision Scale
Bibliographic record
Abstract
Evaluation research provides new perspectives for clinical supervision (CS), and international collaboration offers advantages to develop valid instruments for this purpose. Besides translation, an instrument developed and tested in another culture requires systematic validation. The study focuses on the translation process of the Manchester Clinical Supervision Scale for testing in Finland carried out collaboratively between the Universities of Tampere and Manchester. The instrument is a 45-item questionnaire with a Likert-type (1-5) scale comprising seven sub-scales: trust and rapport, supervisor advice and support, improved care and skills, importance and value of CS, finding time, personal issues and reflection and total score. At first, a licensed translator translated the instrument into Finnish. A native British language teacher at the University language centre performed the blind back-translation into English. The translations were compared by both collaborative parties and by three experienced Finnish supervisors. A pilot sample (n = 182) was collected to test the translated instrument. In this sample Cronbach's alpha value for the total score was 0.9227 and in the sub-scales 0.6393-0.8838. The mean values in the sub-scales were 14.2-29.3, SDs 3.02-3.88 and modes 14.0-30.0. The British test sample had almost similar values. Translating an instrument into another language not only requires expertise in language, but also in practice. The cultural validation is the most important phase in the process that can be accomplished with pilot testing and statistical methods. However, further expert evaluation is required for the validity of the instrument.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".