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Record W3205742301 · doi:10.2196/30804

Status of Compassionate, Respectful, and Caring Health Service Delivery: Scoping Review

2021· article· en· W3205742301 on OpenAlexvenueno aff
Adane Nigusie, Berhanu Fikadie Endehabtu, Dessie Abebaw Angaw, Alemayehu Teklu, Zeleke Abebaw Mekonnen, Marta Feletto, Abraham Assan, Assegid Samuel, Kabir Sheikh, Binyam Tilahun

Bibliographic record

VenueJMIR Human Factors · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsnot available
FundersAlliance for Health Policy and Systems ResearchUniversity of GondarWorld Health Organization
KeywordsHealth careThematic analysisNursingMedicineMEDLINECochrane LibraryService delivery frameworkService (business)Medical educationPolitical scienceAlternative medicineQualitative researchBusinessSociology

Abstract

fetched live from OpenAlex

BACKGROUND: A compassionate, respectful, and caring (CRC) health professional is very important for human-centered care, serving clients ethically and with respect, adhering to the professional oath, and serving as a model for young professionals. As countries try to achieve universal health coverage (UHC), quality delivery of health services is crucial. CRC health care is an initiative around the need to provide quality care services to clients and patients. However, there is an evidence gap on the status of CRC health care service delivery. OBJECTIVE: This scoping review aimed to map global evidence on the status of CRC health service delivery practice. METHODS: An exhaustive literature review and Delphi technique were used to answer the 2 research questions: "What is the current status of CRC health care practices among health workers?" and "Is it possible for health professionals, health managers, administrators, and policy makers to incorporate it into their activity while designing strategies that could improve the humanistic and holistic approach to health care provision?" The studies were searched from the year 2014 to September 2020 using electronic databases such as MEDLINE (PubMed), Cochrane Library, Web of Science, Hinari, and the World Health Organization (WHO) library. Additionally, grey literature such as Google, Google Scholar, and WorldWideScience were scrutinized. Studies that applied any study design and data collection and analysis methods related to CRC care were included. Two authors extracted the data and compared the results. Discrepancies were resolved by discussion, or the third reviewer made the decision. Findings from the existing literature were presented using thematic analysis. RESULTS: A total of 1193 potentially relevant studies were generated from the initial search, and 20 studies were included in the final review. From this review, we identified 5 thematic areas: the status of CRC implementation, facilitators for CRC health care service delivery, barriers to CRC health care delivery, disrespectful and abusive care encountered by patients, and perspectives on CRC. The findings of this review indicated that improving the mechanisms for monitoring health facilities, improving accountability, and becoming aware of the consequences of maltreatment within facilities are critical steps to improving health care delivery practices. CONCLUSIONS: This scoping review identified that there is limited CRC service provision. Lack of training, patient flow volume, and bed shortages were found to be the main contributors of CRC health care delivery. Therefore, the health care system should consider the components of CRC in health care delivery during in-service training, pre-service training, monitoring and evaluation, community engagement, workload division, and performance appraisal.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.088
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.088
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0230.026
Science and technology studies0.0020.003
Scholarly communication0.0090.006
Open science0.0030.003
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.164
GPT teacher head0.503
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations12
Published2021
Admission routes1
Has abstractyes

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