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Record W2909876699 · doi:10.3934/medsci.2019.1.33

Older adults’ activity on a geriatric hospital unit: A behavioral mapping study

2019· article· en· W2909876699 on OpenAlexafffund
Patrocinio Ariza‐Vega, Hattie Shu, Ruvini Amarasekera, Nicola Edwards, Marta Filipski, Dolores Langford, Kenneth Madden, Maureen C. Ashe

Bibliographic record

VenueAIMS Medical Science · 2019
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsVancouver Coastal HealthSimon Fraser UniversityUniversity of British Columbia
FundersCanada Research Chairs
KeywordsObservational studySittingMedicineVisitor patternDementiaActivities of daily livingGeriatricsUnit (ring theory)GerontologyPhysical medicine and rehabilitationPhysical therapyPsychologyDiseasePsychiatry

Abstract

fetched live from OpenAlex

<em>Background:</em> Systematic reviews highlight a preponderance of prolonged sedentary behavior in the hospital setting, with possible consequences for patients’ health and mobility. To date, most of the published literature in this field focus on the hospital experience for older adults with dementia or stroke. Few data describe hospital activity patterns in specialized geriatric units for frail older adults, who are already at risk of spending prolonged periods of time sitting. Yet, promoting older adults’ activity throughout hospitalization, when possible, is an avenue for exploration to identify opportunities to encourage more daily functional activities, and minimize the risk of post-hospital syndrome. <em>Methods:</em> This was a two-part observational study to describe (1) the hospital indoor environment and (2) patients’ activity patterns (using behavioral mapping) within public areas of two hospital units. One combined-trained physiotherapist and occupational therapist recorded information on indoor environmental features for two acute geriatric hospital units, such as potential opportunities for sitting and walking (i.e., handrails, chairs, benches, etc.), and identified obstacles which may impede activity (i.e., food or laundry carts in hallways, etc.). The observer also systematically scanned these units every 15 minutes (8 am to 4 pm) over two days/unit (one weekday and one weekend day) using standard behavioral mapping methods. There were three to four observation stations identified on each unit to count the number of people who were present, distinguish their role (patient, visitor), approximate age, gender, and body position or activity (sitting, standing, walking). We did not enter patients’ rooms. We described units’ indoor environment, and observed activity for each unit. We used Chi square tests to compare differences in observations between units, day of the week, and gender. <em>Results:</em> For both units there were similar indoor environmental features, with the exception of the floorplans, number of beds, minor differences in flooring materials, and an additional destination room (two lounges attached to one unit). Both units had items such as laundry carts against walls in hallways, blocking handrails, when present. We observed between 46–86% (average 60%) of admitted patients in the public areas of hospital units, with variability depending on unit and day: More than half of the observations were of patients sitting. Approximately 20% of patients were observed more than once: This included five women and seven men. There were significant associations for gender and observations on weekdays (men &gt; women; Chi square = 17.01, <em>p</em> &lt; 0.0001), and weekend days (women &gt; men; Chi square = 6.11, <em>p</em> = 0.013). There were more visitor observations on Unit 2. <em>Conclusions:</em> These exploratory findings are an opportunity to, generate hypotheses for future testing, and act as a starting point to collaborate with front line clinicians to highlight the indoor environment’s role in promoting activity, and develop future strategies to safely introduce more activity into the acute care setting for older adults.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.325
Teacher spread0.303 · 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

Citations5
Published2019
Admission routes2
Has abstractyes

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