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Enregistrement W2115724345 · doi:10.1186/1472-698x-12-27

Experiences, opportunities and challenges of implementing task shifting in underserved remote settings: the case of Kongwa district, central Tanzania

2012· article· en· W2115724345 sur OpenAlexfundno aff
Michael Munga, Stella Kilima, Prince Mutalemwa, William Kisoka, Mwelecele N. Malecela

Notice bibliographique

RevueBMC International Health and Human Rights · 2012
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Maternal and Child Health
Établissements canadiensnon disponible
Organismes subventionnairesCanadian Institutes of Health ResearchHealth CanadaInternational Development Research CentrePublic Health Agency of CanadaGovernment of CanadaPublic Health Agency
Mots-clésTanzaniaContext (archaeology)Task (project management)Public relationsMedicinePublic healthHealth policyNursingEconomic growthPolitical scienceSocioeconomicsSociologyGeographyManagement

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Tanzania is experiencing acute shortages of Health Workers (HWs), a situation which has forced health managers, especially in the underserved districts, to hastily cope with health workers' shortages by adopting task shifting. This has however been due to limited options for dealing with the crisis of health personnel. There are on-going discussions in the country on whether to scale up task shifting as one of the strategies for addressing health personnel crisis. However, these discussions are not backed up by rigorous scientific evidence. The aim of this paper is two-fold. Firstly, to describe the current situation of implementing task shifting in the context of acute shortages of health workers and, secondly, to provide a descriptive account of the potential opportunities or benefits and the likely challenges which might ensue as a result of implementing task shifting. METHODS: We employed in-depth interviews with informants at the district level and supplemented the information with additional interviews with informants at the national level. Interviews focussed on the informants' practical experiences of implementing task shifting in their respective health facilities (district level) and their opinions regarding opportunities and challenges which might be associated with implementation of task shifting practices. At the national level, the main focus was on policy issues related to management of health personnel in the context of implementation of task shifting, in addition to seeking their opinions and perceptions regarding opportunities and challenges of implementing task shifting if formally adopted. RESULTS: Task shifting has been in practice for many years in Tanzania and has been perceived as an inevitable coping mechanism due to limited options for addressing health personnel shortages in the country. Majority of informants had the concern that quality of services is likely to be affected if appropriate policy infrastructures are not in place before formalising tasks shifting. There was also a perception that implementation of task shifting has ensured access to services especially in underserved remote areas. Professional discontent and challenges related to the management of health personnel policies were also perceived as important issues to consider when implementing task shifting practices. Additional resources for additional training and supervisory tasks were also considered important in the implementation of task shifting in order to make it deliver much the same way as it is for conventional modalities of delivering care. CONCLUSIONS: Task shifting implementation occurs as an ad hoc coping mechanism to the existing shortages of health workers in many undeserved areas of the country, not just in the study site whose findings are reported in this paper. It is recommended that the most important thing to do now is not to determine whether task shifting is possible or effective but to define the limits of task shifting so as to reach a consensus on where it can have the strongest and most sustainable impact in the delivery of quality health services. Any action towards this end needs to be evidence-based.

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)0,000
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: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,713
Score d'incertitude au seuil0,803

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
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 Opus0,083
Tête enseignante GPT0,340
Écart entre enseignants0,257 · 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'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

Citations72
Publié2012
Routes d'admission1
Résumé présentoui

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