Pattern and perception of wellbeing, quality of work life and quality of care of health professionals in Southwest Nigeria
Notice bibliographique
Résumé
BACKGROUND: Personal wellbeing (PW) including quality of life and work life is a very complex concept that influences health professionals' commitment and productivity. Improving PW may result in positive outcomes and good quality of care. Therefore, this study aimed to assess the pattern and perception of wellbeing, quality of work life (QoWL) and quality of care (QoC) of health professionals (HPs) in southwest Nigeria. METHODS: The study was a convergent parallel mixed method design comprising a cross-sectional survey (1580 conveniently selected participants) and a focus group interview (40 purposively selected participants). Participants' PW, quality of life (QoL), QoWL, and QoC were assessed using the PW Index Scale, 5-item World Health Organization Well-Being Index, QoWL questionnaire, and Clinician QoC scale, respectively. The pattern of wellbeing, QoWL and quality of care of HPs were evaluated using t-test and ANOVA tests. Binary regression analysis was used to assess factors that could classify participants as having good or poor wellbeing, QoWL, and quality of care of HPs. The qualitative findings were thematically analyzed following two independent transcriptions. An inductive approach to naming themes was used. Codes were assigned to the data and common codes were grouped into categories, leading to themes and subthemes. RESULTS: Of 1600 administered questionnaires, 1580 were returned, giving a 98.75% response rate. Only 45.3%, 43.9%, 39.8% and 38.4% of HP reported good PW, QoL, QoC and QoWL, respectively; while 54.7%, 56.1%, 60.2% and 61.6% were poor. There were significant gender differences in PW and QoC in favor of females. With an increase in age and years of practice, there was a significant increase in PW, QoWL and QoC. As the work volume increased, there was significant decrease in QoWL. Participants with master's or Ph.D. degrees reported improved QoWL while those with diploma reported better QoC. PWI and QoC were significantly different along the type of appointment, with those who held part-time appointments having the least values. The regression models showed that participant's characteristics such as age, gender, designation, and work volume significantly classified health professionals who had good or poor QoC, QoWL, PW and QoL. The focus group interview revealed four themes and 16 sub-themes. The four themes were the definitions of QoC, QoWL, and PW, and dimensions of QoC. CONCLUSION: More than half of health professionals reported poor quality of work life, quality of life and personal wellbeing which were influenced by personal and work-related factors. All these may have influenced the poor quality of care reported, despite the finding of a good knowledge of what quality of care entails.
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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 | 0,001 | 0,003 |
| 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 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».