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Record W4321600263 · doi:10.1186/s13643-023-02188-3

Capacity-building and continuing professional development in healthcare and rehabilitation in low- and middle-income countries—a scoping review protocol

2023· article· en· W4321600263 on OpenAlexafffund
Dalton Deprez, Angela J Busch, Paola Andrea Ramírez, Eliany Pedrozo Araque, Julia Bidonde

Bibliographic record

VenueSystematic Reviews · 2023
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsUniversity of Saskatchewan
FundersNorwegian Institute of Public HealthUniversity of Saskatchewan
KeywordsMedicineCapacity buildingGrey literaturePsychological interventionProtocol (science)RehabilitationHealth careProcess (computing)Medical educationKnowledge managementMEDLINENursingComputer scienceAlternative medicinePathologyEconomic growth

Abstract

fetched live from OpenAlex

BACKGROUND: A recent world health report suggests that there is a growing rehabilitation human resource crisis. This review focuses on the capacity-building needed to meet present and future rehabilitation challenges in low- and middle-income countries (LMICs). Capacity-building is the process by which individuals and organizations obtain, improve, and retain the skills, knowledge, tools, equipment, and other resources needed to do their jobs competently. The objectives of this review are (1) to determine how capacity-building has been defined, implemented, and evaluated in LMICs and (2) to provide an overview of the effectiveness of capacity-building initiatives. METHODS: In the first of seven stages, we will refine and delimit the research. Then, we will identify relevant studies by searching five biomedical databases, two rehabilitation databases, three regional databases, and three databases of gray literature. Two independent reviewers will then select the studies using a priori selection criteria. We will exclude incomplete records, records published prior to 2000 for databases and 2010 for gray literature, and records written in languages other than English or Spanish. We will also exclude records focusing on entry-to-practice programs in academic settings. For Objective 1, using qualitative analysis software, we will extract and analyze text from included records that define or explains capacity building. For Objective 2, using an online file-sharing platform, one reviewer will extract data describing the effectiveness of capacity-building interventions and a second reviewer will verify the accuracy, with disagreements resolved by consensus. The results will be collated using tables and charts. After synthesizing the results, we will discuss the practicality and applicability of the findings with partners from Honduras and Colombia. We will use several formats and venues including presentations and publications in English and Spanish to present our results. DISCUSSION: To our knowledge, this will be the first attempt to systematically identify knowledge of capacity-building and rehabilitation in LMICs. This scoping review results will offer unique insights concerning the breadth and depth of literature in the area. It is anticipated that results from this scoping review will guide efforts in future capacity-building efforts in rehabilitation in LMICs. REVIEW REGISTRATION: Busch AJ, Deprez D, Bidonde J, Ramírez PA, Araque EP. Capacity building and continuing professional development in healthcare and rehabilitation in low- and middle-income countries-a scoping review. 2021. https://doi.org/10.17605/OSF.IO/7VGXU .

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)0.022
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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: Protocol · Consensus signal: Protocol
Teacher disagreement score0.240
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.156
GPT teacher head0.524
Teacher spread0.368 · 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.

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

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

Citations22
Published2023
Admission routes2
Has abstractyes

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