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Record W7051491631

PEMETAAN PELAYANAN APOTEK MENGGUNAKAN SERVICE AREADI KECAMATAN RAJABASA

2024· other· id· W7051491631 on OpenAlexaff

Bibliographic record

VenueDigilib Repository Unila (Lampung University) · 2024
Typeother
Languageid
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPopulationChristian ministrySample (material)
DOInot available

Abstract

fetched live from OpenAlex

Kecamatan Rajabasa sebagai kecamatan dengan jumlah apotek terbanyak di wilayah utara Kota Bandar Lampung tidak didukung dengan informasi berbasis spasial berupa lokasi dan persebaran apoteknya. Tidak tersedianya informasi terkait apotek di Kecamatan Rajabasa dapat menimbulkan permasalahan seperti berapa jumlah apotek yang seharusnya ada di Kecamatan Rajabasa apabila disesuaikan dengan jumlah penduduknya, dan tidak jelasnya informasi terkait luas jangkauan pelayanan dari masing-masing apotek di Kecamatan Rajabasa. Penelitian ini bertujuan untuk mengetahui jumlah apotek yang ada di Kecamatan Rajabasa sudah sesuai dengan jumlah penduduknya atau belum dan untuk mengetahui luas pelayanan (service area) apotek di Kecamatan Rajabasa. Variabel dalam penelitian ini adalah apotek, dengan teknik pengumpulan data yaitu survei dan dokumentasi. Penelitian ini menggunakan teknik analisis data overlay dan network analyst. Hasil penelitian menunjukkan jumlah apotek yang ada di Kecamatan Rajabasa memenuhi atau sudah sesuai dengan jumlah penduduknya, dimana kebutuhan jumlah apotek di Kecamatan Rajabasa adalah sebanyak 7 unit, sedangkan di Kecamatan Rajabasa terdapat 21 unit apotek atau oversupply sebanyak 14 unit. Hampir semua area dan permukiman di Kecamatan Rajabasa dapat terjangkau oleh pelayanan apotek, dimana dari 12,97 km2 luas Kecamatan Rajabasa hanya 4,01 km2 (30,91%) yang tidak terjangkau oleh pelayanan apotek. Sedangkan 8,96 km2 (69,09%) area dan permukiman di Kecamatan Rajabasa masuk jangkauan pelayanan apotek, dengan rincian 4,91 km2 (37,86%) masuk kategori sangat terjangkau, serta 4,05 km2 (31,23%) masuk kategori terjangkau. Kata Kunci: pemetaan, apotek, populasi penduduk pendukung, luas pelayanan Rajabasa District as a sub-district with the largest number of pharmacies in the Northern Region of Bandar Lampung City is not supported by spatial-based information in the form of location and distribution of pharmacies. The unavailability of information related to pharmacies in Rajabasa Subdistrict can cause problems such as how many pharmacies should be in Rajabasa Subdistrict when adjusted to the population, and unclear information regarding the service coverage area of each pharmacy in Rajabasa Subdistrict. This study aims to determine the number of pharmacies in Rajabasa District in accordance with the population or not and to determine the service area of pharmacies in Rajabasa District. The variables in this study are pharmacies, with data collection techniques, namely surveys and documentation. This study uses overlay data analysis techniques and network analyst. The results showed that the number of pharmacies in Rajabasa District met or was in accordance with the population, where the need for the number of pharmacies in Rajabasa District was 7 units, while in Rajabasa District there were 21 pharmacy units or an oversupply of 14 units. Almost all areas and settlements in Rajabasa Subdistrict can be reached by pharmacy services, where of the 12.97 km2 area of Rajabasa Subdistrict only 4.01 km2 (30.91%) are not reached by pharmacy services. While 8.96 km2 (69.09%) of areas and settlements in Rajabasa Sub-district are within the reach of pharmacy services, with details of 4.91 km2 (37.86%) in the very affordable category, and 4.05 km2 (31.23%) in the affordable category. Keywords: mapping, pharmacy, threshold, service area

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.974
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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