The Munich ChronoType Questionnaire for Shift-Workers (MCTQ <sup>Shift</sup> )
Bibliographic record
Abstract
Sleep is systematically modulated by chronotype in day-workers. Therefore, investigations into how shift-work affects sleep, health, and cognition may provide more reliable insights if they consider individual circadian time (chronotype). The Munich ChronoType Questionnaire (MCTQ) is a useful tool for determining chronotype. It assesses chronotype based on sleep behavior, specifically on the local time of mid-sleep on free days corrected for sleep debt accumulated over the workweek (MSFsc). Because the original MCTQ addresses people working standard hours, we developed an extended version that accommodates shift-work (MCTQ(Shift)). We first present the validation of this new version with daily sleep logs (n = 52) and actimetry (n = 27). Next, we evaluated 371 MCTQ(Shift) entries of shift-workers (rotating through 8-h shifts starting at 0600 h, 1400 h, and 2200 h). Our results support experimental findings showing that sleep is difficult to initiate and to maintain under the constraints of shift-work. Sleep times are remarkably stable on free days (on average between midnight and 0900 h), so that chronotype of shift-workers can be assessed by means of MSF-similar to that of day-workers. Sleep times on free-days are, however, slightly influenced by the preceding shift (displacements <1 h), which are smallest after evening shifts. We therefore chose this shift-specific mid-sleep time (MSF(E)) to assess chronotype in shift-workers. The distribution of MSF(E) in our sample is identical to that of MSF in day-workers. We propose conversion algorithms for chronotyping shift-workers whose schedules do not include free days after evening shifts.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".