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Effective Transfer of Nutritional Messages to Caregivers by Health Service Providers Trained in Counseling in Two Poor Districts of the Lambayeque Region, Peru

2017· article· en· W4389023047 on OpenAlexaff
Myriam B. Charron, Grace S. Marquis, Hilary Creed‐Kanashiro, Rosario Bartolini Martinez, Cindy Castro Sernaqué

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill UniversitySte. Anne's HospitalUniversité Sainte-Anne
Fundersnot available
KeywordsMedicineEnvironmental healthTrainerGovernment (linguistics)Service (business)NursingBusiness

Abstract

fetched live from OpenAlex

Suboptimal infant and young child feeding practices contribute to poor nutrition outcomes. The 2015 Peru Demographic Health Survey reported that 49% of infants 6–8 months did not consume meats, poultry, fish, or eggs and 36% received fewer than three food categories. These inadequate feeding practices contribute to the high rates of anemia (43.5% of children 6 – 35 months). In response, the Lambayeque regional government funded a train‐the‐trainer initiative in the four poorest districts of the region to improve the skills of health service professionals (HP) in effective nutrition education counselling. The objectives of the present study were to evaluate how the training was currently applied in counselling by HP and to identify influential factors on effective transfer of improved nutrition practices to caregivers. Of particular interest was to examine interpersonal relationships between HP and caregivers. Data were collected in six health establishments in two of the four selected Lambayeque districts. We completed in‐depth interviews with 5 HP, and 34 exit interviews with caregivers, of whom 7 gave in‐depth interviews and 5 who also permitted home observations. In addition, 29 direct participant observations of visits to the child growth and development monitoring program were conducted to observe the counselling. Counselling messages focused on how to use multi‐micronutrient powders and increase consumption of iron‐rich foods (e.g., liver, sangrecita ). A majority of caregivers identified those same messages as the most important ones to assure their children's health. Interviews with caregivers and home observations indicated that nutritional messages (e.g., give more iron‐rich foods, give more legumes) were acceptable and feasible to achieve in the household. Learning materials (e.g., model plates) and group demonstration sessions facilitated caregiver comprehension, whereas caregivers' Quechua language was a barrier. Challenges that affected the continuity of good counselling in the health services were the frequent rotation of health service personnel and poor caregiver attendance. Lastly, beliefs or traditions prevented the adoption of some messages (e.g., duck is a cold meat, consuming animal blood ( sangrecita ) is comparable to consuming the animal's soul). Health professionals in the targeted districts used counselling skills demonstrated in the on‐going training and delivered the training's key nutrition messages. The HP made use of learning materials and used different delivery methods; caregivers understood the nutritional messages and applied them in their home. To conclude, HP also benefited from this on‐going initiative as they manifested a sense of satisfaction in what they had accomplished in improving children's health and nutritional status. Support or Funding Information McBurney Foundation

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)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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 designObservational
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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