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Enregistrement W7128126826

FEASIBILITY & COST-IMPLICATION ANALYSIS OF THE CARER-INCLUSIVE ORGANIZATIONAL STANDARD AS AN INTERVENTION

2022· dissertation· en· W7128126826 sur OpenAlexaboutno aff
Regina Ding

Notice bibliographique

RevueMacSphere (McMaster University) · 2022
Typedissertation
Langueen
DomaineSocial Sciences
ThématiqueIntergenerational Family Dynamics and Caregiving
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIntervention (counseling)Psychological interventionPopulation ageingMental healthAnxietyWork (physics)PopulationHealth care
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Canada’s aging demographics has produced a crisis in which the working age population is increasingly required to provide unpaid care for aging friends, family or relatives while maintaining employment responsibilities. Currently, there are 8.1 million carers in Canada, with 6.1 million of these carers simultaneously managing their own careers/paid employment alongside these care duties. This dual role of carer and employee places physical and mental burden on a growing number of carer-employees, and is associated with adverse effects on both health and work performance. The literature suggests that adverse effects such as stress, depression, anxiety are associated with caregiving burden, and can manifest in physical symptoms affecting health such as sleep deprivation or fatigue. Additionally, care-work conflict may impede work responsibilities by increasing absenteeism/presentism, reducing productivity, delaying career development and early retirement from the workforce. As a result of these adverse consequences, an understanding of the role the employer plays in supporting carer-employees needs is critical, as these arrangements are mutually beneficial for both employer and carer-employee. However, evidence of the effectiveness of workplace interventions for carers is nascent; additional investigation is needed in order to bridge this gap and encourage widespread uptake of carer initiatives in the private sector. In this dissertation study, an intervention is implemented within a large-sized workplace. We evaluate the following questions: 1) How has COVID-19 impacted the workplace and the nature of caregiving?; 2) What are the gaps within the workplace pertaining to baseline carer-supportive workplace culture?; 3) Does our designed intervention improve work and health outcomes of employees?; 4) Is the intervention cost-saving from the employer’s perspective? These research questions contribute to the paucity of knowledge on this topic as well as providing actionable evidence and tools for employers and policymakers to stimulate change. We found that with the transition to remote working during COVID, carers were struggling with the work-life balance due to the undefined boundaries between work, care and personal life, this effect was exacerbated by the closure of community carer supports, thereby increasing feelings of isolation. However, flexibility and privacy was gained as a result of this arrangement. In designing a tailored intervention, we highlighted that within our partnered workplace, carers had significantly less coworker support, and employee-rated family supportive supervisor behaviour was dispersed across all potential score ranges. As a result, our designed intervention focused on generating a supportive and approachable work culture for carers, centered around education and consciousness-raising. With the implementation of the intervention however, we found mixed results. We did not observe significant changes in employee health and work outcome variables post-intervention compared to pre-intervention, nor did we find the intervention to be cost-saving. However, carers and managers/HR communicated their positive informative experiences with the intervention and highlighted its capacity of practicality in the future. These findings in conjunction suggests that the intervention may be a starting point for culture change, however, further research is needed across a variety of contexts.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,019
score de la tête « metaresearch » (Gemma)0,044
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,026
Score d'incertitude au seuil0,102

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0190,044
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,004
Bibliométrie0,0020,002
Études des sciences et des technologies0,0020,002
Communication savante0,0030,003
Science ouverte0,0030,004
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0220,001

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.

Tête enseignante Opus0,010
Tête enseignante GPT0,281
Écart entre enseignants0,271 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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