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Record W2899902753 · doi:10.1007/s10943-018-0728-6

Religion, Spirits, Human Agents and Healing: A Conceptual Understanding from a Sociocultural Study of Tehuledere Community, Northeastern Ethiopia

2018· article· en· W2899902753 on OpenAlexfundno aff
Mesfin Haile Kahissay, Teferi Gedif Fenta, Heather Boon

Bibliographic record

VenueJournal of Religion and Health · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
FundersAddis Ababa UniversityUniversity of Toronto
KeywordsGlobeEthnographyFocus groupQualitative researchNarrativePsychological interventionSociocultural evolutionHealth carePsychologySociologyGender studiesMedicineNursingSocial sciencePolitical scienceAnthropologyArt

Abstract

fetched live from OpenAlex

This paper explores the relationship among religion, spirits and healing in the Tehuledere community in the northeastern part of Ethiopia and focuses on how this knowledge can inform primary healthcare reform. The study employed qualitative ethnographic methods. Participatory observation, over a total of 5 months during the span of 1 year, was supplemented by focus group discussions (96 participants in 10 groups) and in-depth interviews (n = 20) conducted with key informants. Data were analyzed thematically using narrative strategies. The present study revealed that members of the study community perceive health, illness and healing as being given by God. Many of the Tehuledere people attribute illness to the wrath of supernatural forces. Healing is thought to be mitigated by divine assistance obtained through supplication and rituals and through the healing interventions of nature spirit actors. We found that the health, illnesses and healing were inextricably linked to religious and spiritual beliefs. Our findings suggest that religious and spiritual elements should be considered when drafting and implementing primary healthcare strategies for the study communities and similar environments and populations around the globe.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.227
GPT teacher head0.453
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2018
Admission routes1
Has abstractyes

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