MétaCan
Menu
Back to cohort
Record W2904287081 · doi:10.1186/s40814-018-0377-2

Feasibility study of goal setting discussions between older adults and volunteers facilitated by an eHealth application: development of the Health TAPESTRY approach

2018· article· en· W2904287081 on OpenAlexafffundabout
Dena Javadi, Larkin Lamarche, Ernie Avilla, Raied Siddiqui, Jessica Gaber, Mehreen Bhamani, Doug Oliver, Laura Cleghorn, Dee Mangin, Lisa Dolovich

Bibliographic record

VenuePilot and Feasibility Studies · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsMcMaster University
FundersHealth CanadaGovernment of OntarioOntario Ministry of Health and Long-Term CareMcMaster University
KeywordsGoal Attainment ScalingGoal settingeHealthPsychologyHealth careIntervention (counseling)PopulationNursingMedical educationPsychological interventionSet (abstract data type)GerontologyMedicineFamily medicineApplied psychologyComputer science

Abstract

fetched live from OpenAlex

In keeping with the changing needs of the Canadian population, primary care systems need to become more person-focused in providing quality care to older adults. As part of Health TAPESTRY, a complex intervention to strengthen primary care for older adults, a goal setting exercise was developed and tested in an initial feasibility study, intended to foster collaboration between patients and providers. Participants—clinic clients—were recruited from the McMaster Family Health Team in Hamilton, Ontario. Five participants took part in the goal setting feasibility study phase I, which tested the functionality of a technology-enabled goal setting exercise between older adults and volunteers. Based on observations and feedback from volunteers, interprofessional team members, and older adults, the exercise was refined to include a guided survey and goals report. The goal setting survey is a list of probing questions designed based on SMART (specific, measurable, attainable, relevant, timely) goal setting strategies and goal attainment scaling (GAS). This was used in phase II, carried out with 16 participants, where the feasibility of goal setting and goal attainment with support from volunteers and interprofessional teams was tested. Volunteers carried out the goal setting survey via a tablet computer, a report of client goals was generated and sent to interprofessional teams, and client goals were discussed during clinic huddles. At 6 months of follow-up, clients self-evaluated their progress using GAS. The goal setting exercise in phase I took an average of 24:45 (SD 11:42) minutes and yielded a diverse set of life and health goals. Goals identified by older adults were primarily focused on the maintenance of a certain level of activity or health state. Phase I work resulted in important changes to the goal setting process (e.g., asking about goal setting later in conversation, changing wording of questions) and development of a summary report of goals sent to the interprofessional team. In phase II, 44 goals were set by 16 participants during an average 7:23 (SD 4:26) minute discussion. Of these goals, 43.9% were characterized as health goals while 63.4% were characterized as life goals. Under the umbrella of Life goals, productivity featured most prominently at 22.9% of all goals. Goal attainment was not measured in phase I. In phase II, clients had an average weighted goal attainment score of 51.5. Considering client preferences for one goal over another, 68.8% of clients, on average, at least partially achieved the goals they had set. Goal setting as part of the Health TAPESTRY approach was feasible and provided interprofessional teams with client narratives that helped improve care management for older adults. The overall intervention—including the refined goal setting component—is being scaled and evaluated in a pragmatic randomized controlled trial.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.160
GPT teacher head0.451
Teacher spread0.291 · 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 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".

Quick stats

Citations15
Published2018
Admission routes3
Has abstractyes

Explore more

Same venuePilot and Feasibility StudiesSame topicGeriatric Care and Nursing HomesFrench-language works237,207