A prospective pilot investigation of the Zulu translation of the CMCC Neck Disability Index Questionnaire and Short Form McGill Pain Questionnaire with respect to its concurrent validity when compared to their English counterparts
Bibliographic record
Abstract
Neck pain is a common problem, globally, as well as in South Africa. Zulu is the first language of a very large proportion of the South African population, and as such, addressing the needs of this population group with respect to neck pain is a priority. Many reliable pain indexes exist in English to record the degree of disability with regards to neck pain. These are invaluable tools in aiding the health practitioner to assess the progress of treatment and the severity of the patient's disability. Two of the most credible and frequently used indexes are the Canadian Memorial Chiropractic College Neck Disability Index (CMCC NDl) and the Short Form McGill Pain Questionnaire (SFMPQ). However, no such scale exists in Zulu. The purpose of this pilot investigation was, firstly, to analyze and critique the Zulu translations of the CMCC NDl and the SFMPQ in order to establish their face validity. Secondly, to establish their concurrent validity ensuring that the translated questionnaires are specific and sensitive enough to use as tools in data collection when compared to their English counterparts. Thirdly, to make recommendations for further improvement in terms of the Zulu questionnaires and lastly, to make recommendations for further studies for improvement in terms of the use of these questionnaires as research tools amongst the Zulu speaking population of South Africa. Firstly, the CMCC NDl and the SFMP questionnaires were translated into Zulu by means of a focus group. These versions were then assessed by means of a focus (or discussion) group, to assess their face validity. Changes were made to the original translations according to the recommendations of this group. These versions were then assessed with regards to their concurrent validity with the original English versions. Fifty volunteers, who were literate in both English and Zulu and who have suffered with neck pain, filled in both the Zulu and English versions of both questionnaires.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".