The experience of traineeships in the EU
Notice bibliographique
Résumé
The experience of traineeships in the EU" 5 KEY FINDINGSAround half of respondents report having had a traineeship (46%). More young people report having had a traineeship (46%) than an apprenticeship or student job (both 26%).Only a third of young people have had none of these experiences (32%).Traineeships are most common in Cyprus and the Netherlands (both 79%) and least common in Lithuania and Slovakia (both 8%). Respondents in France (41%), Finland (36%) and the Netherlands (31%) were most likely to have had more than three traineeships.Almost six out of ten respondents did not receive any financial compensation during their last (i.e.most recent) traineeship (59%). Among those who received financial compensation, less than half say this financial compensation was sufficient to cover basic living costs (46%). The highest number of respondents to receive financial compensation is in Slovenia (81%) while the lowest is found in Belgium (19%). Around three quarters of respondents said they were covered by insurance (73%).The majority of trainees think that their experience was or would be useful to find a regular job (71%), but more than one in four disagree (28%). The share of those who felt the traineeship helped or would help finding a job was highest in Romania and Ireland (both 85%) followed by Belgium, Spain, and Portugal (all 83%), while the share of those who felt that their last traineeship did or would not help finding a job was highest in Poland (43%), Cyprus (37%), Lithuania (36%) and Germany (34%).A high share of respondents has had multiple traineeships. Amongst those that have had at least one traineeship experience, 38% answered that they had only one traineeship, 22% had two, 14% had three, and 21% had four or more traineeshipsThe quality of the last traineeship was often better than that of another previous one 3 . In particular, while 10% of respondents said that they did not learn things that are useful professionally in the last traineeship, almost one in five (18%) report the same for another one undertaken previously. A similar pattern is visible in another question: while in the last traineeship 9% of respondents could not turn to a mentor that helped them and explained how to do the work, this share rises to 11% for a previous traineeship.3 Respondents who had more than one traineeship were asked more questions about one of their other traineeship experiences, which was randomly chosen.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».