MétaCan
Menu
Retour à la cohorte
Enregistrement W2561909666 · doi:10.5339/qfarc.2016.hbpp2358

Shedding Light on the Roots of Dissatisfaction with Health Care Services in the State of Qatar: An Exploratory Study

2016· article· en· W2561909666 sur OpenAlexaboutno aff
Catherine Nasrallah, Yara Qutteina, Salma M. Khaled

Notice bibliographique

RevueQatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1 · 2016
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHealth careContext (archaeology)MedicinePublic healthPopulationNursingInterviewFamily medicineGerontologyEnvironmental healthGeography

Résumé

récupéré en direct d'OpenAlex

Introduction Dissatisfaction with health care performance is an important source of information about health care reforms as perceived by the public as it is associated with negative beliefs about health system. Previous studies have shown that dissatisfaction with health care has a long-term negative impact on the health care users' relationship with healthcare providers, health related behaviors, and health outcomes. In addition, a recent study conducted in Qatar, showed that approximately 24% of the studied population who used health care in the past 12 months prior to the study were dissatisfied with health care services provided in the country. Given that dissatisfaction with care can negatively impact on help-seeking behaviors, this finding could have grave public health implications. This has been witnessed in the context of high prevalence of chronic health conditions in Qatar where long-term relations with healthcare professionals are necessary for better chronic disease management, reduced disease-related complications, and mortality. This study aims to identify the sources of dissatisfaction with medical care among adults, Qataris and white collar migrants aged eighteen years or older. Methods This study is based on secondary data from a larger national survey, which was conducted during the fall of 2012 for the purpose of collecting household-based information on health services utilization and health-related expenditures. Disproportionate stratified probability sampling was employed to select a representative sample of households. A final sample of 3,080 completed face-to-face interviews (1,528 Qataris and 1,552 White Collar Migrants) using computer assisted personal interviewing (CAPI) method for a raw response rate of 78.1%. The sample included individuals who may or may not have used Qatar's health care system during the 12 months prior to survey administration. Respondents were asked to discuss the reasons for their discontent with healthcare services in Qatar by selecting pre-coded categories of dissatisfaction including: Waiting time to see the provider, language used to communicate, clarity of how things are explained to the patient, poor services provided (such as cleanliness, reception, respect, and parking), inability to choose provider or doctor, high costs and other reasons to be specified by respondents. A total of 711 open-ended responses to the “Other” category were translated, coded and analyzed qualitatively. “Crowdedness”, “staff and physicians' incompetence”, “medical errors”, “discrimination”, “disrespect”, and “lack of staff and services” are all themes that emerged as reasons for dissatisfaction. Analysis Arabic responses were translated into English and researchers discussed any dissimilar results until an agreement was reached on all translated responses. Upon reviewing the responses, themes, which were different from the pre-specified answer choices of the questionnaire, emerged. The researchers then coded the responses by assigning codes to each response, then compared against each other. Coding discrepancy was discussed until an agreement was reached. The codes of the open-ended responses were later merged with those of the pre-specified categories and the corresponding frequency for each coding category was calculated using STATA. The Alberta Quality Matrix for Health was used to guide the analysis of the themes based on the six dimensions of health system quality. Results The analysis of the open-ended responses that probed into reasons for respondents' dissatisfaction revealed thirteen categories of dissatisfaction that were related to four different dimensions of quality of healthcare, based on the Alberta Quality Matrix for Health. The most common dimension of dissatisfaction with health care in Qatar was accessibility, which refers to the provision of health service in the most optimum setting and within “reasonable time and distance”. Safety was the second most common dimension reported by the respondents. This construct relates to minimizing any threats that could cause harm. Acceptability, such as the provision of respectful and patient-centered health services was the third dimension identified, followed by efficiency, which is mainly related to the optimal use of resources, to achieve the best desired health outcomes. Conclusion Identifying the roots of dissatisfaction with health care services among distinct social groups can be achieved by analyzing responses to simple open-ended questions in routinely administered population health surveys. This is important for monitoring the quality of care in heterogeneous population contexts as well as engaging the public in the process of developing a world-class health care system as per Qatar's national vision of 2030. This research highlights priority needs to be addressed by the Qatari government in order to increase health care satisfaction as part of the quest for better health care in the country.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,201
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,076
Tête enseignante GPT0,361
Écart entre enseignants0,285 · 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 tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
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
Publié2016
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueQatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1Même sujetHealthcare Policy and ManagementTravaux en français237 207