Evaluation by employees of employee management on large US dairy farms
Notice bibliographique
Résumé
Employees, many of whom are not native English speakers, perform the majority of work on large US dairy farms. Although management of employees is a critical role of dairy owners and managers, factors that improve employee engagement and retention are not well known. Objectives were to (1) identify key dairy farm employee management issues based on employee perceptions, (2) evaluate strengths and weaknesses of farms based on employee responses, (3) investigate differences between Latino and English-speaking employees, and (4) investigate differences in perception between employers and employees. Employees from 12 US dairy farms (each with a minimum of 10 employees) were interviewed by phone following a questionnaire provided. Employees provided their responses to 21 Likert scale questions and 8 open-ended questions. There was a wide range in employee turnover among farms (<10 to >100%). Latino employees had much shorter tenure and were more often employed in milking and livestock care than English-speaking employees. Employee perceptions differed among farms regarding whether they would recommend their farm as a place to work, teamwork within the dairy, whether rules were fairly applied, availability of tools and equipment, clear lines of supervision, and recognition for good work in the previous 15 d. Latino employees (n = 91) were more positive in many of these measures than their English-speaking counterparts (n = 77) but less often provided ideas to their employer on how to improve the business. Employers, surveyed on how they thought their employees would answer, underestimated employee responses on several questions, particularly the interest of employees in learning about dairy. When asked to cite 3 goals of the operation, there were differences among owners, managers, and employees. Although employees rated their commitment to the farm and their interest in learning as high, based on turnover, there was an obvious disparity between reality and ideal employee management. Consequently, employers should act on identified management shortfalls to improve employee retention.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».