Managing hidden illnesses that impact on performance and absenteeism
Notice bibliographique
Résumé
Under-performance and absenteeism are issues that all organisations seek to reduce, often devoting substantial resources to the establishment of performance management policies, extensive training programs and a host of complementary policies. However, while most contributory factors to these unwelcome workplace issues are known and tackled, one factor stands alone as a key contributor to sub-optimal performance and poor attendance, namely mental illness. This paper oultines the extent of mental illness in western countries, why it is hidden, and how the use of additional policies can be adopted to assist employees who choose not to divulge to their employer that they have a mental illness. Under-performance and absenteeism are issues that all organisations seek to reduce, often devoting substantial resources to the establishment of performance management policies, extensive training programs and a host of complementary policies. However, while most contributory factors to these unwelcome workplace issues are known and tackled, one factor stands alone as a key contributor to sub-optimal performance and poor attendance, namely mental illness. This factor, while acknowledged is seriously under-valued in size and breadth of coverage in the workplace with few operational managers knowing that in any calendar year somewhere between a fifth and a quarter of their staff will have a mental illness. The effect of mental illness on fitness to work is not well known in management literature as its incidence is shrouded in secrecy and subterfuge by its sufferers, most of whom seek to attribute changes to performance and attendance to other factors. Consequently, performance management plans will rarely have this information available, even when employees are directly asked if they have a health problem affecting their fitness for work. The pervasive nature of stigma that surrounds mental illness keeps it hidden and away from public view and the wider community, few people knowing that in any 12-month period mental illness accounts for 20-27% of any community (NIMHa, 2011; ABS, 2007; Wittchen and Jacobi, 2005). Fears of a negative backlash after disclosing such illness is so powerful, that employees with temporary or long-term mental illness would rather attribute performance issues to other problems rather than to run the risk of being viewed with the prevailing stereotype that surrounds sufferers of these types of illnesses. Hence, when performance issues arise, such employees are often resistant to the agreed goals of performance plans, often to the frustration of operational managers and human resource staff alike. This paper will outline the incidence of mental illnesses in the workplace, showing why it is concealed and common effects of mental illness on employee performance and attendance. Strategies to effectively manage employees with a hidden mental illness will be reviewed and outlined, Currently both management and human resource literature fail to provide adequately for the management of employees with a hidden mental illness.
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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 | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».