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Record W3187638552 · doi:10.3389/ijph.2021.620425

State of Research Quality and Knowledge Transfer and Translation and Capacity Strengthening Strategies for Sound Health Policy Decision-Making in Palestine

2021· article· en· W3187638552 on OpenAlexafffund
Mohammed Alkhaldi, Hamza Meghari, Irene Jillson, Abdulsalam Alkaiyat, Marcel Tanner

Bibliographic record

VenueInternational Journal of Public Health · 2021
Typearticle
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsMcGill University
FundersMcGill UniversityDeakin UniversityWorld Health Organization
KeywordsGovernment (linguistics)Knowledge translationSnowball samplingPublic healthPublic relationsMedicinePolitical scienceNursingKnowledge management

Abstract

fetched live from OpenAlex

Objectives: Over the last 2 decades, the World Health Organization (WHO) has proposed a global strategy and initiatives to establish a Health Research System (HRS) focusing on Health Research Quality and Standardization (HRQS), Health Research Knowledge Transfer and Dissemination (HRKTD), and Health Research Translation and Utilization into Health Care Decisions and Policies (HRTUDP). Despite the increase in health research productivity over the past several decades, HRS Capacity (HRSC) in Palestine and in the Middle East and North Africa (MENA) region has rarely been objectively evaluated. This study aims at eliciting the perceptions of HRS performers in Palestine in order to understand the status of HRSC, identify gaps, and generate policies and solutions capable of strengthening HRSC in Palestine. Methods: Key informants from three sectors, namely government, academia, and local and international organizations, were selected purposively based on different sampling methods: criterion, critical case, snowball, and homogeneous sampling. Fifty-two in-depth interviews with key informants and a total of fifty-two individuals, participating in six focus groups, were conducted by the principal investigator in Palestine. Data were analyzed by using MAXQDA 12. Results: The overall pattern of the Palestinian HRSC is relatively weak. The key findings revealed that while HR productivity in Palestine is improving, HRQS is at an average level and quality guidelines are not followed due to paucity of understanding, policies, and resources. HRKTD is a central challenge with both a dearth of conceptualization of translational science and inadequate implementation. The factors related to inadequate HRKTD include lack of awareness on the part of the researchers, inadequate regulatory frameworks and mechanisms for both communication and collaboration between and among researchers and policy-makers and clinicians, and lack of availability of, and credibility in, systematized and reliable HR data. Despite the limited knowledge translation, in general, HRTUDP is not considered an essential decision-making methodology mainly due to the lack of interface between knowledge producers (researchers) and users (policymakers), understanding level, HR credibility and availability of applied research, and governance, resources, and political fluctuations. Recommendations to strengthen HRS in Palestine include: a consolidated research regulatory framework and an effective capacity strengthening strategy overseen by Palestinian authorities; the promotion of HRQS and concepts and practices of translational science; and, most importantly, the use of findings for evidence-based policies and practice. Conclusion: Strengthening HRSC is both an imperative step and an opportunity to improve the Palestinian health system and ensure it is based on research evidence and knowledge. Building a successful HRS characterized by capacities of high-quality research and well-disseminated and translated knowledge is a prerequisite to effective health systems and services. This can be achieved by political commitment to support such strengthening, a consolidated leadership and governance structure, and a strong operational capacity strengthening strategy.

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)0.018
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.563
GPT teacher head0.611
Teacher spread0.048 · 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 designOther design
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

Citations18
Published2021
Admission routes2
Has abstractyes

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