MétaCan
Menu
Back to cohort
Record W4386251444 · doi:10.1186/s43058-023-00477-5

Assessing ad-hoc adaptations’ alignment with therapeutic goals: a qualitative study of lay counselor-delivered family therapy in Eldoret, Kenya

2023· article· en· W4386251444 on OpenAlexfundno aff
Bonnie N. Kaiser, Julia H. Kaufman, Jonathan T. Wall, Elsa A. Friis Healy, David Ayuku, Gregory A. Aarons, Eve S. Puffer

Bibliographic record

VenueImplementation Science Communications · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
FundersQuality Enhancement Research InitiativeNational Institute of Mental HealthDuke Global Health Institute, Duke UniversityNational Institutes of HealthJosiah Charles Trent Memorial FoundationWashington University in St. LouisGrand Challenges CanadaU.S. Department of Veterans Affairs
KeywordsPost hocPost-hoc analysisQualitative researchPsychologyPsychotherapistMedicineSociologyAnthropologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: A key question in implementation science is how to balance adaptation and fidelity in translating interventions to new settings. There is growing consensus regarding the importance of planned adaptations to deliver interventions in contextually sensitive ways. However, less research has examined ad-hoc adaptations, or those that occur spontaneously in the course of intervention delivery. A key question is whether ad-hoc adaptations ultimately contribute to or detract from intervention goals. This study aimed to (a) identify ad-hoc adaptations made during delivery of a family therapy intervention and (b) assess whether they promoted or interrupted intervention goals. METHODS: Tuko Pamoja (Swahili: "We are Together") is an evidence-informed family therapy intervention aiming to improve family dynamics and mental health in Kenya. Tuko Pamoja employs a task-shifting model, delivered by lay counselors who are afforded a degree of flexibility in presenting content and in practices they use in sessions. We used transcripts of therapy sessions with 14 families to examine ad-hoc adaptations used by counselors. We first identified and characterized ad-hoc adaptations through a team-based code development, coding, and code description process. Then, we evaluated to what extent ad-hoc adaptations promoted the principles and strategies of the intervention ("TP-promoting"), disrupted them ("TP-interrupting"), or neither ("TP-neutral"). To do this, we first established inter-coder agreement on application of these categories with verification by the intervention developer. Then, coders categorized ad-hoc adaptation text segments as TP-promoting, TP-interrupting, or TP-neutral. RESULTS: Ad-hoc adaptations were frequent and included (in decreasing order): incorporation of religious content, exemplars/role models, community dynamics and resources, self-disclosure, and metaphors/proverbs. Ad-hoc adaptations were largely TP-promoting (49%) or neutral (39%), but practices were TP-interrupting 12% of the time. TP-interrupting practices most often occurred within religious content and exemplars/role models, which were also the most common practices overall. CONCLUSION: Extra attention is needed during planned adaptation, training, and supervision to promote intervention-aligned use of common ad-hoc adaptation practices. Discussing them in trainings can provide guidance for lay providers on how best to incorporate ad-hoc adaptations during delivery. Future research should evaluate whether well-aligned ad-hoc adaptations improve therapeutic outcomes. TRIAL REGISTRATION: Pilot trial registered at clinicaltrials.gov (C0058).

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)0.011
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.018
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.009
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0020.002
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 head0.756
GPT teacher head0.731
Teacher spread0.025 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueImplementation Science CommunicationsSame topicHealth Policy Implementation ScienceFrench-language works237,207